BeamGA Median: A Hybrid Heuristic Search Approach

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style="font-size:.9rem">BeamGA Median: A Hybrid Heuristic Search Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghada%20Badr">Ghada Badr</a>, <a href="https://publications.waset.org/abstracts/search?q=Manar%20Hosny"> Manar Hosny</a>, <a href="https://publications.waset.org/abstracts/search?q=Nuha%20Bintayyash"> Nuha Bintayyash</a>, <a href="https://publications.waset.org/abstracts/search?q=Eman%20Albilali"> Eman Albilali</a>, <a href="https://publications.waset.org/abstracts/search?q=Souad%20Larabi%20Marie-Sainte"> Souad Larabi Marie-Sainte</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The median problem is significantly applied to derive the most reasonable rearrangement phylogenetic tree for many species. More specifically, the problem is concerned with finding a permutation that minimizes the sum of distances between itself and a set of three signed permutations. Genomes with equal number of genes but different order can be represented as permutations. In this paper, an algorithm, namely BeamGA median, is proposed that combines a heuristic search approach (local beam) as an initialization step to generate a number of solutions, and then a Genetic Algorithm (GA) is applied in order to refine the solutions, aiming to achieve a better median with the smallest possible reversal distance from the three original permutations. In this approach, any genome rearrangement distance can be applied. In this paper, we use the reversal distance. To the best of our knowledge, the proposed approach was not applied before for solving the median problem. Our approach considers true biological evolution scenario by applying the concept of common intervals during the GA optimization process. This allows us to imitate a true biological behavior and enhance genetic approach time convergence. We were able to handle permutations with a large number of genes, within an acceptable time performance and with same or better accuracy as compared to existing algorithms. <iframe src="https://publications.waset.org/abstracts/73026.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=median%20problem" title="median problem">median problem</a>, <a href="https://publications.waset.org/abstracts/search?q=phylogenetic%20tree" title=" phylogenetic tree"> phylogenetic tree</a>, <a href="https://publications.waset.org/abstracts/search?q=permutation" title=" permutation"> permutation</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=beam%20search" title=" beam search"> beam search</a>, <a href="https://publications.waset.org/abstracts/search?q=genome%20rearrangement%20distance" title=" genome rearrangement distance"> genome rearrangement distance</a> </p> <a href="https://publications.waset.org/abstracts/73026/beamga-median-a-hybrid-heuristic-search-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73026.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">265</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" 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BeamGA Median: A Hybrid Heuristic Search Approach