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parameters</title> <meta name="description" content="Search results for: genetic parameters"> <meta name="keywords" content="genetic parameters"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="genetic parameters"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 10120</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: genetic parameters</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10120</span> Reproductive Traits for Holstein Cattle </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashraf%20M.%20Ward">Ashraf M. Ward</a>, <a href="https://publications.waset.org/abstracts/search?q=Ruban%20S.%20Yu"> Ruban S. Yu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data consisting of 2757 records from tow Holstein herds made between 2000 and 2010 were used to examine environmental factors affecting age at first calving (AFC) and calving intervals (CI) and consequently estimate genetic and phenotypic parameters and trends. The overall means and standard errors for AFC and CI were 39.4 ± 7.2 months and 487.5 ± 151.6 days respectively. The respective heritability estimates were 0.091 ± 0.05 and 0.044 ± 0.032, while the repeatability estimate for CI was 0.096 ± 0.001. The genetic trends for CI and AFC were -0.6 d/yr and -0.01 mo/yr respectively and were both significant (P < 0.001), indicating a decrease in mean breeding value over the study period. Phenotypic trends were -0.31 mo/yr and -0.35 d/yr for AFC and CI respectively though non-significant (P > 0.05). The low heritability for CI and AFC indicated that temporary environmental influences were much greater than genetic influences or permanent environmental influences on these traits. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Holstein" title="Holstein">Holstein</a>, <a href="https://publications.waset.org/abstracts/search?q=reproductive" title=" reproductive"> reproductive</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20parameters" title=" genetic parameters"> genetic parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=heritability" title=" heritability "> heritability </a> </p> <a href="https://publications.waset.org/abstracts/18509/reproductive-traits-for-holstein-cattle" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18509.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">720</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10119</span> Modeling of Tool Flank Wear in Finish Hard Turning of AISI D2 Using Genetic Programming</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=V.%20Pourmostaghimi">V. Pourmostaghimi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Zadshakoyan"> M. Zadshakoyan </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Efficiency and productivity of the finish hard turning can be enhanced impressively by utilizing accurate predictive models for cutting tool wear. However, the ability of genetic programming in presenting an accurate analytical model is a notable characteristic which makes it more applicable than other predictive modeling methods. In this paper, the genetic equation for modeling of tool flank wear is developed with the use of the experimentally measured flank wear values and genetic programming during finish turning of hardened AISI D2. Series of tests were conducted over a range of cutting parameters and the values of tool flank wear were measured. On the basis of obtained results, genetic model presenting connection between cutting parameters and tool flank wear were extracted. The accuracy of the genetically obtained model was assessed by using two statistical measures, which were root mean square error (RMSE) and coefficient of determination (R²). Evaluation results revealed that presented genetic model predicted flank wear over the study area accurately (R² = 0.9902 and RMSE = 0.0102). These results allow concluding that the proposed genetic equation corresponds well with experimental data and can be implemented in real industrial applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cutting%20parameters" title="cutting parameters">cutting parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=flank%20wear" title=" flank wear"> flank wear</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20programming" title=" genetic programming"> genetic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=hard%20turning" title=" hard turning"> hard turning</a> </p> <a href="https://publications.waset.org/abstracts/96967/modeling-of-tool-flank-wear-in-finish-hard-turning-of-aisi-d2-using-genetic-programming" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/96967.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">178</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10118</span> Genetic Parameters as Indicators of Sustainability and Diversity of Schinus terebinthifolius Populations in the Riparian Area of the São Francisco River</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Renata%20Silva-Mann">Renata Silva-Mann</a>, <a href="https://publications.waset.org/abstracts/search?q=Sheila%20Val%C3%A9ria%20%C3%81lvares%20Carvalho"> Sheila Valéria Álvares Carvalho</a>, <a href="https://publications.waset.org/abstracts/search?q=Rob%C3%A9rio%20Anast%C3%A1cio%20Ferreira"> Robério Anastácio Ferreira</a>, <a href="https://publications.waset.org/abstracts/search?q=Laura%20Jane%20Gomes"> Laura Jane Gomes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There is growing interest in defining indicators of sustainability, which are important for monitoring the conservation of native forests, particularly in areas of permanent protection. These indicators are references for assessing the state of the forest and the status of the depredated area and its ability to maintain species populations. The aim of the present study was to select genetic parameters as indicators of sustainability for Schinus terebinthifolius Raddi. Fragments located in riparian areas between the Sergipe and Alagoas States in Brazil. This species has been exploited for traditional communities, which represent 20% of the incoming. This study was carried out using the indicators suggested by the Organization for Economic Cooperation and Development, which were identified as Driving-Pressure-State-Impact-Response (DPSIR) factors. The genetic parameters were obtained in five populations located on the shores and islands of the São Francisco River, one of the most important rivers in Brazil. The framework for Schinus conservation suggests seventeen indicators of sustainability. In accordance with genetic parameters, the populations are isolated, and these genetic parameters can be used to monitor the sustainability of those populations in riparian area with the aim of defining strategies for forest restoration. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=alleles" title="alleles">alleles</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20markers" title=" molecular markers"> molecular markers</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20diversity" title=" genetic diversity"> genetic diversity</a>, <a href="https://publications.waset.org/abstracts/search?q=biodiversity" title=" biodiversity"> biodiversity</a> </p> <a href="https://publications.waset.org/abstracts/1658/genetic-parameters-as-indicators-of-sustainability-and-diversity-of-schinus-terebinthifolius-populations-in-the-riparian-area-of-the-sao-francisco-river" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1658.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">303</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10117</span> Genetic Algorithm Based Deep Learning Parameters Tuning for Robot Object Recognition and Grasping</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Delowar%20Hossain">Delowar Hossain</a>, <a href="https://publications.waset.org/abstracts/search?q=Genci%20Capi"> Genci Capi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper concerns with the problem of deep learning parameters tuning using a genetic algorithm (GA) in order to improve the performance of deep learning (DL) method. We present a GA based DL method for robot object recognition and grasping. GA is used to optimize the DL parameters in learning procedure in term of the fitness function that is good enough. After finishing the evolution process, we receive the optimal number of DL parameters. To evaluate the performance of our method, we consider the object recognition and robot grasping tasks. Experimental results show that our method is efficient for robot object recognition and grasping. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20recognition" title=" object recognition"> object recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=robot%20grasping" title=" robot grasping"> robot grasping</a> </p> <a href="https://publications.waset.org/abstracts/67943/genetic-algorithm-based-deep-learning-parameters-tuning-for-robot-object-recognition-and-grasping" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67943.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">353</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10116</span> Application of Genetic Algorithm with Multiobjective Function to Improve the Efficiency of Photovoltaic Thermal System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sonveer%20Singh">Sonveer Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanjay%20Agrawal"> Sanjay Agrawal</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20V.%20Avasthi"> D. V. Avasthi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jayant%20Shekhar"> Jayant Shekhar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this paper is to improve the efficiency of photovoltaic thermal (PVT) system with the help of Genetic Algorithms with multi-objective function. There are some parameters that affect the efficiency of PVT system like depth and length of the channel, velocity of flowing fluid through the channel, thickness of the tedlar and glass, temperature of inlet fluid i.e. all above parameters are considered for optimization. An attempt has been made to the model and optimizes the parameters of glazed hybrid single channel PVT module when two objective functions have been considered separately. The two objective function for optimization of PVT module is overall electrical and thermal efficiency. All equations for PVT module have been derived. Using genetic algorithms (GAs), above two objective functions of the system has been optimized separately and analysis has been carried out for two cases. Two cases are: Case-I; Improvement in electrical and thermal efficiency when overall electrical efficiency is optimized, Case-II; Improvement in electrical and thermal efficiency when overall thermal efficiency is optimized. All the parameters that are used in genetic algorithms are the parameters that could be changed, and the non-changeable parameters, like solar radiation, ambient temperature cannot be used in the algorithm. It has been observed that electrical efficiency (14.08%) and thermal efficiency (19.48%) are obtained when overall thermal efficiency was an objective function for optimization. It is observed that GA is a very efficient technique to estimate the design parameters of hybrid single channel PVT module. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title="genetic algorithm">genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=energy" title=" energy"> energy</a>, <a href="https://publications.waset.org/abstracts/search?q=exergy" title=" exergy"> exergy</a>, <a href="https://publications.waset.org/abstracts/search?q=PVT%20module" title=" PVT module"> PVT module</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/16503/application-of-genetic-algorithm-with-multiobjective-function-to-improve-the-efficiency-of-photovoltaic-thermal-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16503.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">605</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10115</span> Genetic Variation of Autosomal STR Loci from Unrelated Individual in Iraq </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Imad">H. Imad</a>, <a href="https://publications.waset.org/abstracts/search?q=Q.%20Cheah"> Q. Cheah</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Mohammad"> J. Mohammad</a>, <a href="https://publications.waset.org/abstracts/search?q=O.%20Aamera"> O. Aamera </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this study is twofold. One is to determine the genetic structure of Iraq population and the second objective of the study was to evaluate the importance of these loci for forensic genetic purposes. FTA® Technology (FTA™ paper DNA extraction) utilized to extract DNA. Twenty STR loci and Amelogenin including D3S1358, D13S317, PentaE, D16S539, D18S51, D2S1338, CSF1PO, Penta D, THO1, vWA, D21S11, D7S820, TPOX, D8S1179, FGA, D2S1338, D5S818, D6S1043, D12S391, D19S433, and Amelogenin amplified by using power plex21® kit. PCR products detected by genetic analyzer 3730xL then data analyzed by PowerStatsV1.2. Based on the allelic frequencies, several statistical parameters of genetic and forensic efficiency have been estimated. This includes the homozygosity and heterozygosity, effective number of alleles (n), the polymorphism information content (PIC), the power of discrimination (DP), and the power of exclusion (PE). The power of discrimination values for all tested loci was from 75% to 96% therefore, those loci can be safely used to establish a DNA-based database for Iraq population. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autosomal%20STR" title="autosomal STR">autosomal STR</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20variation" title=" genetic variation"> genetic variation</a>, <a href="https://publications.waset.org/abstracts/search?q=Middle%20and%20South%20of%20Iraq" title=" Middle and South of Iraq"> Middle and South of Iraq</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20parameters" title=" statistical parameters"> statistical parameters</a> </p> <a href="https://publications.waset.org/abstracts/1682/genetic-variation-of-autosomal-str-loci-from-unrelated-individual-in-iraq" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1682.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">385</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10114</span> Genetic and Non-Genetic Evaluation of Milk Yield and Litter Size of Awassi Sheep in Drylands</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Al-Najjar">Khaled Al-Najjar</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Q.%20Al-Momani"> Ahmad Q. Al-Momani</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Elnahas"> Ahmed Elnahas</a>, <a href="https://publications.waset.org/abstracts/search?q=Reda%20Elsaid"> Reda Elsaid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The research was carried out using records of Awassi sheep bred in drylands at Al-Fjaj Station, Jordan. That aimed to study non-genetic factors affecting milk yield (MK), litter size at birth (LZB); estimate heritability, repeatability, and genetic and phenotypic correlation using SAS and MTDFREML programs. The results were as follows, the average MK and LZB were 92.84 (kg) and 1.16, respectively. MK was highly significantly affected by each parity, age of ewe, year of lambing, and lactation period, while only the year of lambing had a significant effect on LZB. The heritability and repeatability were 0.07 and 0.10 for MK, while it was 0.05 and 0.25 for LZB. The genetic and phenotypic correlations were 0.17 and 0.02 between MK and LZB, respectively. The research concluded that the herd is genetically homozygous and therefore needs to increase genetic variance by introducing LZB-improved rams and selecting females from dams who achieved at least four parties to increase returns in drylands. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Awassi%20sheep" title="Awassi sheep">Awassi sheep</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20parameters" title=" genetic parameters"> genetic parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=litter%20size" title=" litter size"> litter size</a>, <a href="https://publications.waset.org/abstracts/search?q=milk%20yield" title=" milk yield"> milk yield</a> </p> <a href="https://publications.waset.org/abstracts/149418/genetic-and-non-genetic-evaluation-of-milk-yield-and-litter-size-of-awassi-sheep-in-drylands" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149418.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">119</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10113</span> A Survey of Grammar-Based Genetic Programming and Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Matthew%20T.%20Wilson">Matthew T. Wilson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper covers a selection of research utilizing grammar-based genetic programming, and illustrates how context-free grammar can be used to constrain genetic programming. It focuses heavily on grammatical evolution, one of the most popular variants of grammar-based genetic programming, and the way its operators and terminals are specialized and modified from those in genetic programming. A variety of implementations of grammatical evolution for general use are covered, as well as research each focused on using grammatical evolution or grammar-based genetic programming on a single application, or to solve a specific problem, including some of the classically considered genetic programming problems, such as the Santa Fe Trail. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=context-free%20grammar" title="context-free grammar">context-free grammar</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithms" title=" genetic algorithms"> genetic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20programming" title=" genetic programming"> genetic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=grammatical%20evolution" title=" grammatical evolution"> grammatical evolution</a> </p> <a href="https://publications.waset.org/abstracts/120249/a-survey-of-grammar-based-genetic-programming-and-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/120249.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">187</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10112</span> Level of Awareness of Genetic Counselling in Benue State Nigeria: Its Advocacy on the Inheritance of Sickle Cell Disease</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Agi%20Sunday">Agi Sunday</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A descriptive analysis of reported cases of sickle cell disease and the level of awareness about genetic counselling in 30 hospitals were carried out. Additionally, 150 individuals between ages 16-45 were randomly selected for evaluation of genetic counselling awareness. The main tools for this study were questionnaires which were taken to hospitals, and individuals completed the others. The numbers of reported cases of sickle cell disease recorded in private, public and teaching hospitals were 14 and 57; 143 and 89; 272 and 57 for the periods of 1995-2000 and 2001-2005, respectively. A general informal genetic counselling took place mostly in the hospitals visited. 122 (86%) individuals had the knowledge of genetic disease and only 43 (30.3%) individuals have been exposed to genetic counselling. 64% of individuals agreed that genetic counselling would help in the prevention of genetic disease. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sickle%20disease" title="sickle disease">sickle disease</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20counseling" title=" genetic counseling"> genetic counseling</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20testing" title=" genetic testing"> genetic testing</a>, <a href="https://publications.waset.org/abstracts/search?q=advocacy" title=" advocacy"> advocacy</a> </p> <a href="https://publications.waset.org/abstracts/8810/level-of-awareness-of-genetic-counselling-in-benue-state-nigeria-its-advocacy-on-the-inheritance-of-sickle-cell-disease" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8810.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">389</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10111</span> Rainfall-Runoff Forecasting Utilizing Genetic Programming Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Najah%20Ahmed%20Al-Mahfoodh">Ahmed Najah Ahmed Al-Mahfoodh</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Najah%20Ahmed%20%20Al-Mahfoodh"> Ali Najah Ahmed Al-Mahfoodh</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Al-Shafie"> Ahmed Al-Shafie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, genetic programming (GP) technique has been investigated in prediction of set of rainfall-runoff data. To assess the effect of input parameters on the model, the sensitivity analysis was adopted. To evaluate the performance of the proposed model, three statistical indexes were used, namely; Correlation Coefficient (CC), Mean Square Error (MSE) and Correlation of Efficiency (CE). The principle aim of this study is to develop a computationally efficient and robust approach for predict of rainfall-runoff which could reduce the cost and labour for measuring these parameters. This research concentrates on the Johor River in Johor State, Malaysia. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20programming" title="genetic programming">genetic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=rainfall-runoff" title=" rainfall-runoff"> rainfall-runoff</a>, <a href="https://publications.waset.org/abstracts/search?q=Malaysia" title=" Malaysia"> Malaysia</a> </p> <a href="https://publications.waset.org/abstracts/66110/rainfall-runoff-forecasting-utilizing-genetic-programming-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66110.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">481</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10110</span> Material Parameter Identification of Modified AbdelKarim-Ohno Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Martin%20Cermak">Martin Cermak</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomas%20Karasek"> Tomas Karasek</a>, <a href="https://publications.waset.org/abstracts/search?q=Jaroslav%20Rojicek"> Jaroslav Rojicek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The key role in phenomenological modelling of cyclic plasticity is good understanding of stress-strain behaviour of given material. There are many models describing behaviour of materials using numerous parameters and constants. Combination of individual parameters in those material models significantly determines whether observed and predicted results are in compliance. Parameter identification techniques such as random gradient, genetic algorithm, and sensitivity analysis are used for identification of parameters using numerical modelling and simulation. In this paper genetic algorithm and sensitivity analysis are used to study effect of 4 parameters of modified AbdelKarim-Ohno cyclic plasticity model. Results predicted by Finite Element (FE) simulation are compared with experimental data from biaxial ratcheting test with semi-elliptical loading path. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title="genetic algorithm">genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=sensitivity%20analysis" title=" sensitivity analysis"> sensitivity analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20approach" title=" inverse approach"> inverse approach</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20method" title=" finite element method"> finite element method</a>, <a href="https://publications.waset.org/abstracts/search?q=cyclic%20plasticity" title=" cyclic plasticity"> cyclic plasticity</a>, <a href="https://publications.waset.org/abstracts/search?q=ratcheting" title=" ratcheting "> ratcheting </a> </p> <a href="https://publications.waset.org/abstracts/21152/material-parameter-identification-of-modified-abdelkarim-ohno-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21152.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">453</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10109</span> Comparison of ANFIS Update Methods Using Genetic Algorithm, Particle Swarm Optimization, and Artificial Bee Colony</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Michael%20R.%20Phangtriastu">Michael R. Phangtriastu</a>, <a href="https://publications.waset.org/abstracts/search?q=Herriyandi%20Herriyandi"> Herriyandi Herriyandi</a>, <a href="https://publications.waset.org/abstracts/search?q=Diaz%20D.%20Santika"> Diaz D. Santika</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comparison of the implementation of metaheuristic algorithms to train the antecedent parameters and consequence parameters in the adaptive network-based fuzzy inference system (ANFIS). The algorithms compared are genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC). The objective of this paper is to benchmark well-known metaheuristic algorithms. The algorithms are applied to several data set with different nature. The combinations of the algorithms' parameters are tested. In all algorithms, a different number of populations are tested. In PSO, combinations of velocity are tested. In ABC, a different number of limit abandonment are tested. Experiments find out that ABC is more reliable than other algorithms, ABC manages to get better mean square error (MSE) than other algorithms in all data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title="ANFIS">ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20bee%20colony" title=" artificial bee colony"> artificial bee colony</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristic%20algorithm" title=" metaheuristic algorithm"> metaheuristic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/68821/comparison-of-anfis-update-methods-using-genetic-algorithm-particle-swarm-optimization-and-artificial-bee-colony" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68821.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">352</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10108</span> Parameters Tuning of a PID Controller on a DC Motor Using Honey Bee and Genetic Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saeid%20Jalilzadeh">Saeid Jalilzadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> PID controllers are widely used to control the industrial plants because of their robustness and simple structures. Tuning of the controller's parameters to get a desired response is difficult and time consuming. With the development of computer technology and artificial intelligence in automatic control field, all kinds of parameters tuning methods of PID controller have emerged in endlessly, which bring much energy for the study of PID controller, but many advanced tuning methods behave not so perfect as to be expected. Honey Bee algorithm (HBA) and genetic algorithm (GA) are extensively used for real parameter optimization in diverse fields of study. This paper describes an application of HBA and GA to the problem of designing a PID controller whose parameters comprise proportionality constant, integral constant and derivative constant. Presence of three parameters to optimize makes the task of designing a PID controller more challenging than conventional P, PI, and PD controllers design. The suitability of the proposed approach has been demonstrated through computer simulation using MATLAB/SIMULINK. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=controller" title="controller">controller</a>, <a href="https://publications.waset.org/abstracts/search?q=GA" title=" GA"> GA</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=PID" title=" PID"> PID</a>, <a href="https://publications.waset.org/abstracts/search?q=PSO" title=" PSO"> PSO</a> </p> <a href="https://publications.waset.org/abstracts/15526/parameters-tuning-of-a-pid-controller-on-a-dc-motor-using-honey-bee-and-genetic-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15526.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">544</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10107</span> [Keynote Talk]: Machining Parameters Optimization with Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dejan%20Taniki%C4%87">Dejan Tanikić</a>, <a href="https://publications.waset.org/abstracts/search?q=Miodrag%20Mani%C4%87"> Miodrag Manić</a>, <a href="https://publications.waset.org/abstracts/search?q=Jelena%20%C4%90okovi%C4%87"> Jelena Đoković</a>, <a href="https://publications.waset.org/abstracts/search?q=Sa%C5%A1a%20Kalinovi%C4%87"> Saša Kalinović</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the determination of the optimum machining parameters, according to the measured and modelled data of the cutting temperature and surface roughness, during the turning of the AISI 4140 steel. The high cutting temperatures are unwanted occurences in the metal cutting process. They impact negatively on the quality of the machined part. The machining experiments were performed using different cutting regimes (cutting speed, feed rate and depth of cut), with different values of the workpiece hardness, which causes different values of the measured cutting temperature as well as the measured surface roughness. The temperature and surface roughness data were modelled after that using Response Surface Methodology (RSM). The obtained RSM models are used in the process of optimization of the cutting regimes using the Genetic Algorithms (GA) tool, which enables the metal cutting process in the optimum conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithms" title="genetic algorithms">genetic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=machining%20parameters" title=" machining parameters"> machining parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=response%20surface%20methodology" title=" response surface methodology"> response surface methodology</a>, <a href="https://publications.waset.org/abstracts/search?q=turning%20process" title=" turning process"> turning process</a> </p> <a href="https://publications.waset.org/abstracts/82130/keynote-talk-machining-parameters-optimization-with-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82130.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">188</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10106</span> Parameter Estimation of Additive Genetic and Unique Environment (AE) Model on Diabetes Mellitus Type 2 Using Bayesian Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andi%20Darmawan">Andi Darmawan</a>, <a href="https://publications.waset.org/abstracts/search?q=Dewi%20Retno%20Sari%20Saputro"> Dewi Retno Sari Saputro</a>, <a href="https://publications.waset.org/abstracts/search?q=Purnami%20Widyaningsih"> Purnami Widyaningsih</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Diabetes mellitus (DM) is a chronic disease in human that occurred if pancreas cannot produce enough of insulin hormone or the body uses ineffectively insulin hormone which causes increasing level of glucose in the blood, or it was called hyperglycemia. In Indonesia, DM is a serious disease on health because it can cause blindness, kidney disease, diabetic feet (gangrene), and stroke. The type of DM criteria can also be divided based on the main causes; they are DM type 1, type 2, and gestational. Diabetes type 1 or previously known as insulin-independent diabetes is due to a lack of production of insulin hormone. Diabetes type 2 or previously known as non-insulin dependent diabetes is due to ineffective use of insulin while gestational diabetes is a hyperglycemia that found during pregnancy. The most one type commonly found in patient is DM type 2. The main factors of this disease are genetic (A) and life style (E). Those disease with 2 factors can be constructed with additive genetic and unique environment (AE) model. In this article was discussed parameter estimation of AE model using Bayesian method and the inheritance character simulation on parent-offspring. On the AE model, there are response variable, predictor variables, and parameters were capable of representing the number of population on research. The population can be measured through a taken random sample. The response and predictor variables can be determined by sample while the parameters are unknown, so it was required to estimate the parameters based on the sample. Estimation of AE model parameters was obtained based on a joint posterior distribution. The simulation was conducted to get the value of genetic variance and life style variance. The results of simulation are 0.3600 for genetic variance and 0.0899 for life style variance. Therefore, the variance of genetic factor in DM type 2 is greater than life style. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AE%20model" title="AE model">AE model</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20method" title=" Bayesian method"> Bayesian method</a>, <a href="https://publications.waset.org/abstracts/search?q=diabetes%20mellitus%20type%202" title=" diabetes mellitus type 2"> diabetes mellitus type 2</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic" title=" genetic"> genetic</a>, <a href="https://publications.waset.org/abstracts/search?q=life%20style" title=" life style"> life style</a> </p> <a href="https://publications.waset.org/abstracts/73672/parameter-estimation-of-additive-genetic-and-unique-environment-ae-model-on-diabetes-mellitus-type-2-using-bayesian-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73672.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">284</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10105</span> A Review on Parametric Optimization of Casting Processes Using Optimization Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bhrugesh%20Radadiya">Bhrugesh Radadiya</a>, <a href="https://publications.waset.org/abstracts/search?q=Jaydeep%20Shah"> Jaydeep Shah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Indian foundry industry, there is a need of defect free casting with minimum production cost in short lead time. Casting defect is a very large issue in foundry shop which increases the rejection rate of casting and wastage of materials. The various parameters influences on casting process such as mold machine related parameters, green sand related parameters, cast metal related parameters, mold related parameters and shake out related parameters. The mold related parameters are most influences on casting defects in sand casting process. This paper review the casting produced by foundry with shrinkage and blow holes as a major defects was analyzed and identified that mold related parameters such as mold temperature, pouring temperature and runner size were not properly set in sand casting process. These parameters were optimized using different optimization techniques such as Taguchi method, Response surface methodology, Genetic algorithm and Teaching-learning based optimization algorithm. Finally, concluded that a Teaching-learning based optimization algorithm give better result than other optimization techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=casting%20defects" title="casting defects">casting defects</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=parametric%20optimization" title=" parametric optimization"> parametric optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Taguchi%20method" title=" Taguchi method"> Taguchi method</a>, <a href="https://publications.waset.org/abstracts/search?q=TLBO%20algorithm" title=" TLBO algorithm"> TLBO algorithm</a> </p> <a href="https://publications.waset.org/abstracts/21826/a-review-on-parametric-optimization-of-casting-processes-using-optimization-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21826.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">728</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10104</span> Genetic Diversity Based Population Study of Freshwater Mud Eel (Monopterus cuchia) in Bangladesh</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20F.%20Miah">M. F. Miah</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20M.%20A.%20Zinnah"> K. M. A. Zinnah</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20J.%20Raihan"> M. J. Raihan</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Ali"> H. Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20N.%20Naser"> M. N. Naser</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As genetic diversity is most important for existing, breeding and production of any fish; this study was undertaken for investigating genetic diversity of freshwater mud eel, <em>Monopterus cuchia</em> at population level where three ecological populations such as flooded area of Sylhet (P1), open water of Moulvibazar (P2) and open water of Sunamganj (P3) districts of Bangladesh were considered. Four arbitrary RAPD primers (OPB-12, C0-4, B-03 and OPB-08) were screened and RAPD banding patterns were analyzed among the populations considering 15 individuals of each population. In total 174, 138 and 149 bands were detected in the populations of P1, P2 and P3 respectively; however, each primer revealed less number of bands in each population. 100% polymorphic loci were recorded in P2 and P3 whereas only one monomorphic locus was observed in P1, recorded 97.5% polymorphism. Different genetic parameters such as inter-individual pairwise similarity, genetic distance, Nei genetic similarity, linkage distances, cluster analysis and allelic information, etc. were considered for measuring genetic diversity. The average inter-individual pairwise similarity was recorded 2.98, 1.47 and 1.35 in P1, P2 and P3 respectively. Considering genetic distance analysis, the highest distance 1 was recorded in P2 and P3 and the lowest genetic distance 0.444 was found in P2. The average Nei genetic similarity was observed 0.19, 0.16 and 0.13 in P1, P2 and P3, respectively; however, the average linkage distance was recorded 24.92, 17.14 and 15.28 in P1, P3 and P2 respectively. Based on linkage distance, genetic clusters were generated in three populations where 6 clades and 7 clusters were found in P1, 3 clades and 5 clusters were observed in P2 and 4 clades and 7 clusters were detected in P3. In addition, allelic information was observed where the frequency of p and q alleles were observed 0.093 and 0.907 in P1, 0.076 and 0.924 in P2, 0.074 and 0.926 in P3 respectively. The average gene diversity was observed highest in P2 (0.132) followed by P3 (0.131) and P1 (0.121) respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20diversity" title="genetic diversity">genetic diversity</a>, <a href="https://publications.waset.org/abstracts/search?q=Monopterus%20cuchia" title=" Monopterus cuchia"> Monopterus cuchia</a>, <a href="https://publications.waset.org/abstracts/search?q=population" title=" population"> population</a>, <a href="https://publications.waset.org/abstracts/search?q=RAPD" title=" RAPD"> RAPD</a>, <a href="https://publications.waset.org/abstracts/search?q=Bangladesh" title=" Bangladesh"> Bangladesh</a> </p> <a href="https://publications.waset.org/abstracts/45221/genetic-diversity-based-population-study-of-freshwater-mud-eel-monopterus-cuchia-in-bangladesh" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45221.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">505</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10103</span> Dynamic Synthesis of a Flexible Multibody System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Amine%20Ben%20Abdallah">Mohamed Amine Ben Abdallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Imed%20Khemili"> Imed Khemili</a>, <a href="https://publications.waset.org/abstracts/search?q=Nizar%20Aifaoui"> Nizar Aifaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work denotes an insight into dynamic synthesis of multibody systems. A set of mechanism parameters design variable are synthetized based on a desired mechanism response, such as, velocity, acceleration and bodies deformations. Moreover, knowing the work space, for a robot, and mechanism response allow defining optimal parameters mechanism handling with the desired target response. To this end, evolutionary genetic algorithm has been deployed. A demonstrative example for imperfect mechanism has been treated, mainly, a slider crank mechanism with a flexible connecting rod. The transversal deflection of the connecting rod has been chosen as response to identify the mechanism design parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20response" title="dynamic response">dynamic response</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20genetic%20algorithm" title=" evolutionary genetic algorithm"> evolutionary genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20bodies" title=" flexible bodies"> flexible bodies</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/51863/dynamic-synthesis-of-a-flexible-multibody-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51863.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">320</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10102</span> Genetic Testing and Research in South Africa: The Sharing of Data Across Borders</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amy%20Gooden">Amy Gooden</a>, <a href="https://publications.waset.org/abstracts/search?q=Meshandren%20Naidoo"> Meshandren Naidoo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Genetic research is not confined to a particular jurisdiction. Using direct-to-consumer genetic testing (DTC-GT) as an example, this research assesses the status of data sharing into and out of South Africa (SA). While SA laws cover the sending of genetic data out of SA, prohibiting such transfer unless a legal ground exists, the position where genetic data comes into the country depends on the laws of the country from where it is sent – making the legal position less clear. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cross-border" title="cross-border">cross-border</a>, <a href="https://publications.waset.org/abstracts/search?q=data" title=" data"> data</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20testing" title=" genetic testing"> genetic testing</a>, <a href="https://publications.waset.org/abstracts/search?q=law" title=" law"> law</a>, <a href="https://publications.waset.org/abstracts/search?q=regulation" title=" regulation"> regulation</a>, <a href="https://publications.waset.org/abstracts/search?q=research" title=" research"> research</a>, <a href="https://publications.waset.org/abstracts/search?q=sharing" title=" sharing"> sharing</a>, <a href="https://publications.waset.org/abstracts/search?q=South%20Africa" title=" South Africa"> South Africa</a> </p> <a href="https://publications.waset.org/abstracts/153426/genetic-testing-and-research-in-south-africa-the-sharing-of-data-across-borders" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153426.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">161</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10101</span> Genetic Algorithms for Parameter Identification of DC Motor ARMAX Model and Optimal Control</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Mansouri">A. Mansouri</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Krim"> F. Krim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents two techniques for DC motor parameters identification. We propose a numerical method using the adaptive extensive recursive least squares (AERLS) algorithm for real time parameters estimation. This algorithm, based on minimization of quadratic criterion, is realized in simulation for parameters identification of DC motor autoregressive moving average with extra inputs (ARMAX). As advanced technique, we use genetic algorithms (GA) identification with biased estimation for high dynamic performance speed regulation. DC motors are extensively used in variable speed drives, for robot and solar panel trajectory control. GA effectiveness is derived through comparison of the two approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARMAX%20model" title="ARMAX model">ARMAX model</a>, <a href="https://publications.waset.org/abstracts/search?q=DC%20motor" title=" DC motor"> DC motor</a>, <a href="https://publications.waset.org/abstracts/search?q=AERLS" title=" AERLS"> AERLS</a>, <a href="https://publications.waset.org/abstracts/search?q=GA" title=" GA"> GA</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20identification" title=" parameter identification"> parameter identification</a>, <a href="https://publications.waset.org/abstracts/search?q=PID%20speed%20regulation" title=" PID speed regulation"> PID speed regulation</a> </p> <a href="https://publications.waset.org/abstracts/9107/genetic-algorithms-for-parameter-identification-of-dc-motor-armax-model-and-optimal-control" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9107.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">379</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10100</span> Optimal Design of Submersible Permanent Magnet Linear Synchronous Motor Based Design of Experiment and Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiao%20Zhang">Xiao Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Wensheng%20Xiao"> Wensheng Xiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Junguo%20Cui"> Junguo Cui</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongmin%20Wang"> Hongmin Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Submersible permanent magnet linear synchronous motors (SPMLSMs) are electromagnetic devices, which can directly drive plunger pump to obtain the crude oil. Those motors have been gradually applied in oil fields due to high thrust force density and high efficiency. Since the force performance closely depends on the concrete structural parameters, the seven different structural parameters are investigated in detail. This paper presents an optimum design of an SPMLSM to minimize the detent force and maximize the thrust by using design of experiment (DOE) and genetic algorithm (GA). The three significant structural parameters (air-gap length, slot width, pole-arc coefficient) are separately screened using 27 1/16 fractional factorial design (FFD) to investigate the significant effect of seven parameters used in this research on the force performance. Response surface methodology (RSM) is well adapted to make analytical model of thrust and detent force with constraints of corresponding significant parameters and enable objective function to be easily created, respectively. GA is performed as a searching tool to search for the Pareto-optimal solutions. By finite element analysis, the proposed PMLSM shows merits in improving thrust and reducing the detent force dramatically. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=force%20performance" title=" force performance"> force performance</a>, <a href="https://publications.waset.org/abstracts/search?q=design%20of%20experiment%20%28DOE%29" title=" design of experiment (DOE)"> design of experiment (DOE)</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm%20%28GA%29" title=" genetic algorithm (GA)"> genetic algorithm (GA)</a> </p> <a href="https://publications.waset.org/abstracts/72951/optimal-design-of-submersible-permanent-magnet-linear-synchronous-motor-based-design-of-experiment-and-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72951.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">290</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10099</span> Kinematic Hardening Parameters Identification with Respect to Objective Function</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marina%20Franulovic">Marina Franulovic</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20Basan"> Robert Basan</a>, <a href="https://publications.waset.org/abstracts/search?q=Bozidar%20Krizan"> Bozidar Krizan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Constitutive modelling of material behaviour is becoming increasingly important in prediction of possible failures in highly loaded engineering components, and consequently, optimization of their design. In order to account for large number of phenomena that occur in the material during operation, such as kinematic hardening effect in low cycle fatigue behaviour of steels, complex nonlinear material models are used ever more frequently, despite of the complexity of determination of their parameters. As a method for the determination of these parameters, genetic algorithm is good choice because of its capability to provide very good approximation of the solution in systems with large number of unknown variables. For the application of genetic algorithm to parameter identification, inverse analysis must be primarily defined. It is used as a tool to fine-tune calculated stress-strain values with experimental ones. In order to choose proper objective function for inverse analysis among already existent and newly developed functions, the research is performed to investigate its influence on material behaviour modelling. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title="genetic algorithm">genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=kinematic%20hardening" title=" kinematic hardening"> kinematic hardening</a>, <a href="https://publications.waset.org/abstracts/search?q=material%20model" title=" material model"> material model</a>, <a href="https://publications.waset.org/abstracts/search?q=objective%20function" title=" objective function"> objective function</a> </p> <a href="https://publications.waset.org/abstracts/3561/kinematic-hardening-parameters-identification-with-respect-to-objective-function" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3561.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">332</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10098</span> A Review Paper on Data Mining and Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sikander%20Singh%20Cheema">Sikander Singh Cheema</a>, <a href="https://publications.waset.org/abstracts/search?q=Jasmeen%20Kaur"> Jasmeen Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the concept of data mining is summarized and its one of the important process i.e KDD is summarized. The data mining based on Genetic Algorithm is researched in and ways to achieve the data mining Genetic Algorithm are surveyed. This paper also conducts a formal review on the area of data mining tasks and genetic algorithm in various fields. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=KDD" title=" KDD"> KDD</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=descriptive%20mining" title=" descriptive mining"> descriptive mining</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20mining" title=" predictive mining"> predictive mining</a> </p> <a href="https://publications.waset.org/abstracts/43637/a-review-paper-on-data-mining-and-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43637.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">591</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10097</span> Performance Evaluation of Karanja Oil Based Biodiesel Engine Using Modified Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20Bhushan">G. Bhushan</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Dhingra"> S. Dhingra</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20K.%20Dubey"> K. K. Dubey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the evaluation of performance (BSFC and BTE), combustion (P<sub>max</sub>) and emission (CO, NO<sub>x</sub>, HC and smoke opacity) parameters of karanja biodiesel in a single cylinder, four stroke, direct injection diesel engine by considering significant engine input parameters (blending ratio, compression ratio and load torque). Multi-objective optimization of performance, combustion and emission parameters is also carried out in a karanja biodiesel engine using hybrid RSM-NSGA-II technique. The pareto optimum solutions are predicted by running the hybrid RSM-NSGA-II technique. Each pareto optimal solution is having its own importance. Confirmation tests are also conducted at randomly selected few pareto solutions to check the authenticity of the results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title="genetic algorithm">genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=rsm" title=" rsm"> rsm</a>, <a href="https://publications.waset.org/abstracts/search?q=biodiesel" title=" biodiesel"> biodiesel</a>, <a href="https://publications.waset.org/abstracts/search?q=karanja" title=" karanja"> karanja</a> </p> <a href="https://publications.waset.org/abstracts/51865/performance-evaluation-of-karanja-oil-based-biodiesel-engine-using-modified-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51865.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">305</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10096</span> Hardware for Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fariborz%20Ahmadi">Fariborz Ahmadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20Tati"> Reza Tati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Genetic algorithm is a soft computing method that works on set of solutions. These solutions are called chromosome and the best one is the absolute solution of the problem. The main problem of this algorithm is that after passing through some generations, it may be produced some chromosomes that had been produced in some generations ago that causes reducing the convergence speed. From another respective, most of the genetic algorithms are implemented in software and less works have been done on hardware implementation. Our work implements genetic algorithm in hardware that doesn’t produce chromosome that have been produced in previous generations. In this work, most of genetic operators are implemented without producing iterative chromosomes and genetic diversity is preserved. Genetic diversity causes that not only do not this algorithm converge to local optimum but also reaching to global optimum. Without any doubts, proposed approach is so faster than software implementations. Evaluation results also show the proposed approach is faster than hardware ones. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hardware" title="hardware">hardware</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20science" title=" computer science"> computer science</a>, <a href="https://publications.waset.org/abstracts/search?q=engineering" title=" engineering"> engineering</a> </p> <a href="https://publications.waset.org/abstracts/5598/hardware-for-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5598.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">506</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10095</span> Identification of the Parameters of a AC Servomotor Using Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20G.%20Batista">J. G. Batista</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20N.%20Sousa"> K. N. Sousa</a>, <a href="https://publications.waset.org/abstracts/search?q=%C2%ACJ.%20L.%20Nunes"> ¬J. L. Nunes</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20L.%20S.%20Sousa"> R. L. S. Sousa</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20A.%20P.%20Th%C3%A9"> G. A. P. Thé</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work deals with parameter identification of permanent magnet motors, a class of ac motor which is particularly important in industrial automation due to characteristics like applications high performance, are very attractive for applications with limited space and reducing the need to eliminate because they have reduced size and volume and can operate in a wide speed range, without independent ventilation. By using experimental data and genetic algorithm we have been able to extract values for both the motor inductance and the electromechanical coupling constant, which are then compared to measured and/or expected values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=modeling" title="modeling">modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=AC%20servomotor" title=" AC servomotor"> AC servomotor</a>, <a href="https://publications.waset.org/abstracts/search?q=permanent%20magnet%20synchronous%20motor-PMSM" title=" permanent magnet synchronous motor-PMSM"> permanent magnet synchronous motor-PMSM</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20control" title=" vector control"> vector control</a>, <a href="https://publications.waset.org/abstracts/search?q=robotic%20manipulator" title=" robotic manipulator"> robotic manipulator</a>, <a href="https://publications.waset.org/abstracts/search?q=control" title=" control"> control</a> </p> <a href="https://publications.waset.org/abstracts/8166/identification-of-the-parameters-of-a-ac-servomotor-using-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8166.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">470</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10094</span> Control of a Quadcopter Using Genetic Algorithm Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mostafa%20Mjahed">Mostafa Mjahed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper concerns the control of a nonlinear system using two different methods, reference model and genetic algorithm. The quadcopter is a nonlinear unstable system, which is a part of aerial robots. It is constituted by four rotors placed at the end of a cross. The center of this cross is occupied by the control circuit. Its motions are governed by six degrees of freedom: three rotations around 3 axes (roll, pitch and yaw) and the three spatial translations. The control of such system is complex, because of nonlinearity of its dynamic representation and the number of parameters, which it involves. Numerous studies have been developed to model and stabilize such systems. The classical PID and LQ correction methods are widely used. If the latter represent the advantage to be simple because they are linear, they reveal the drawback to require the presence of a linear model to synthesize. It also implies the complexity of the established laws of command because the latter must be widened on all the domain of flight of these quadcopter. Note that, if the classical design methods are widely used to control aeronautical systems, the Artificial Intelligence methods as genetic algorithms technique receives little attention. In this paper, we suggest comparing two PID design methods. Firstly, the parameters of the PID are calculated according to the reference model. In a second phase, these parameters are established using genetic algorithms. By reference model, we mean that the corrected system behaves according to a reference system, imposed by some specifications: settling time, zero overshoot etc. Inspired from the natural evolution of Darwin's theory advocating the survival of the best, John Holland developed this evolutionary algorithm. Genetic algorithm (GA) possesses three basic operators: selection, crossover and mutation. We start iterations with an initial population. Each member of this population is evaluated through a fitness function. Our purpose is to correct the behavior of the quadcopter around three axes (roll, pitch and yaw) with 3 PD controllers. For the altitude, we adopt a PID controller. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=quadcopter" title="quadcopter">quadcopter</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=PID" title=" PID"> PID</a>, <a href="https://publications.waset.org/abstracts/search?q=fitness" title=" fitness"> fitness</a>, <a href="https://publications.waset.org/abstracts/search?q=model" title=" model"> model</a>, <a href="https://publications.waset.org/abstracts/search?q=control" title=" control"> control</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20system" title=" nonlinear system"> nonlinear system</a> </p> <a href="https://publications.waset.org/abstracts/44570/control-of-a-quadcopter-using-genetic-algorithm-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44570.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">431</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10093</span> Estimates of (Co)Variance Components and Genetic Parameters for Body Weights and Growth Efficiency Traits in the New Zealand White Rabbits</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Sakthivel">M. Sakthivel</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Devaki"> A. Devaki</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Balasubramanyam"> D. Balasubramanyam</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Kumarasamy"> P. Kumarasamy</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Raja"> A. Raja</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Anilkumar"> R. Anilkumar</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Gopi"> H. Gopi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The genetic parameters of growth traits in the New Zealand White rabbits maintained at Sheep Breeding and Research Station, Sandynallah, The Nilgiris, India were estimated by partitioning the variance and covariance components. The (co)variance components of body weights at weaning (W42), post-weaning (W70) and marketing (W135) age and growth efficiency traits viz., average daily gain (ADG), relative growth rate (RGR) and Kleiber ratio (KR) estimated on a daily basis at different age intervals (1=42 to 70 days; 2=70 to 135 days and 3=42 to 135 days) from weaning to marketing were estimated by restricted maximum likelihood, fitting six animal models with various combinations of direct and maternal effects. Data were collected over a period of 15 years (1998 to 2012). A log-likelihood ratio test was used to select the most appropriate univariate model for each trait, which was subsequently used in bivariate analysis. Heritability estimates for W42, W70 and W135 were 0.42 ± 0.07, 0.40 ± 0.08 and 0.27 ± 0.07, respectively. Heritability estimates of growth efficiency traits were moderate to high (0.18 to 0.42). Of the total phenotypic variation, maternal genetic effect contributed 14 to 32% for early body weight traits (W42 and W70) and ADG1. The contribution of maternal permanent environmental effect varied from 6 to 18% for W42 and for all the growth efficiency traits except for KR2. Maternal permanent environmental effect on most of the growth efficiency traits was a carryover effect of maternal care during weaning. Direct maternal genetic correlations, for the traits in which maternal genetic effect was significant, were moderate to high in magnitude and negative in direction. Maternal effect declined as the age of the animal increased. The estimates of total heritability and maternal across year repeatability for growth traits were moderate and an optimum rate of genetic progress seems possible in the herd by mass selection. The estimates of genetic and phenotypic correlations among body weight traits were moderate to high and positive; among growth efficiency traits were low to high with varying directions; between body weights and growth efficiency traits were very low to high in magnitude and mostly negative in direction. Moderate to high heritability and higher genetic correlation in body weight traits promise good scope for genetic improvement provided measures are taken to keep the inbreeding at the lowest level. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20parameters" title="genetic parameters">genetic parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=growth%20traits" title=" growth traits"> growth traits</a>, <a href="https://publications.waset.org/abstracts/search?q=maternal%20effects" title=" maternal effects"> maternal effects</a>, <a href="https://publications.waset.org/abstracts/search?q=rabbit%20genetics" title=" rabbit genetics"> rabbit genetics</a> </p> <a href="https://publications.waset.org/abstracts/68262/estimates-of-covariance-components-and-genetic-parameters-for-body-weights-and-growth-efficiency-traits-in-the-new-zealand-white-rabbits" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68262.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">447</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10092</span> Enhancement of Genetic Diversity through Cross Breeding of Two Catfish (Heteropneustes fossilis and Clarias batrachus) in Bangladesh</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20F.%20Miah">M. F. Miah</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Chakrabarty"> A. Chakrabarty</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Two popular and highly valued fish, Stinging catfish (Heteropneustes fossilis) and Asian catfish (Clarias batrachus) are considered for observing genetic enhancement. Cross breeding was performed considering wild and farmed fish through inducing agent. Five RAPD markers were used to assess genetic diversity among parents and offspring of these two catfish for evaluating genetic enhancement in F1 generation. Considering different genetic data such as banding pattern of DNA, polymorphic loci, polymorphic information content (PIC), inter individual pair wise similarity, Nei genetic similarity, genetic distance, phylogenetic relationships, allele frequency, genotype frequency, intra locus gene diversity and average gene diversity of parents and offspring of these two fish were analyzed and finally in both cases higher genetic diversity was found in F1 generation than the parents. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Heteropneustes%20fossilis" title="Heteropneustes fossilis">Heteropneustes fossilis</a>, <a href="https://publications.waset.org/abstracts/search?q=Clarias%20batrachus" title=" Clarias batrachus"> Clarias batrachus</a>, <a href="https://publications.waset.org/abstracts/search?q=cross%20breeding" title=" cross breeding"> cross breeding</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20enhancement" title=" genetic enhancement"> genetic enhancement</a> </p> <a href="https://publications.waset.org/abstracts/82616/enhancement-of-genetic-diversity-through-cross-breeding-of-two-catfish-heteropneustes-fossilis-and-clarias-batrachus-in-bangladesh" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82616.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">251</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10091</span> A Hybrid Genetic Algorithm for Assembly Line Balancing In Automotive Sector</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qazi%20Salman%20Khalid">Qazi Salman Khalid</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Khalid"> Muhammad Khalid</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahid%20Maqsood"> Shahid Maqsood</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a solution for optimizing the cycle time in an assembly line with human-robot collaboration and diverse operators. A genetic algorithm with tailored parameters is used to address the assembly line balancing problem in the automobile sector. A mathematical model is developed, depicting the problem. Currently, the firm runs on the largest candidate rule; however, it causes a lag in orders, which ultimately gets penalized. The results of the study show that the proposed GA is effective in providing efficient solutions and that the cycle time has significantly impacted productivity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=line%20balancing" title="line balancing">line balancing</a>, <a href="https://publications.waset.org/abstracts/search?q=cycle%20time" title=" cycle time"> cycle time</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=productivity" title=" productivity"> productivity</a> </p> <a href="https://publications.waset.org/abstracts/162367/a-hybrid-genetic-algorithm-for-assembly-line-balancing-in-automotive-sector" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162367.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span 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