<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/3136" mdate="2010-04-26 00:00:00"> <author>Felix Dobslaw</author> <title>A ParameterTuning Framework for Metaheuristics Based on Design of Experiments and Artificial Neural Networks</title> <pages>684 - 687</pages> <year>2010</year> <volume>4</volume> <number>4</number> <journal>International Journal of Computer and Information Engineering</journal> <ee>https://publications.waset.org/pdf/3136</ee> <url>https://publications.waset.org/vol/40</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>In this paper, a framework for the simplification and standardization of metaheuristic related parametertuning by applying a four phase methodology, utilizing Design of Experiments and Artificial Neural Networks, is presented. Metaheuristics are multipurpose problem solvers that are utilized on computational optimization problems for which no efficient problem specific algorithm exist. Their successful application to concrete problems requires the finding of a good initial parameter setting, which is a tedious and time consuming task. Recent research reveals the lack of approach when it comes to this so called parametertuning process. In the majority of publications, researchers do have a weak motivation for their respective choices, if any. Because initial parameter settings have a significant impact on the solutions quality, this course of action could lead to suboptimal experimental results, and thereby a fraudulent basis for the drawing of conclusions.</abstract> <index>Open Science Index 40, 2010</index> </article>