Conference Paper
A Genetic Algorithm with Variable Range of Local Search for Tracking Changing Environments.
DOI: 10.1007/354061723X_1002 Conference: Parallel Problem Solving from Nature  PPSN IV, International Conference on Evolutionary Computation. The 4th International Conference on Parallel Problem Solving from Nature, Berlin, Germany, September 2226, 1996, Proceedings
Source: DBLP
 Evolutionary Computation for Dynamic Optimization Problems, Edited by Trung Thanh Nguyen, Shengxiang Yang, Juergen Branke, Xin Yao, 05/2013: chapter 2: pages 3964; SpringerVerlag Berlin Heidelberg., ISBN: 9783642384158

Conference Paper: Genetic algorithms with adaptive immigrants for dynamic environments
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ABSTRACT: One approach integrated with genetic algorithms (GAs) to address dynamic optimization problems (DOPs) is to maintain diversity of the population via introducing immigrants. Many immigrants schemes have been proposed that differ on the way new individuals are generated, e.g., mutating the best individual of the previous environment to generate elitismbased immigrants. This paper examines the performance of elitismbased immigrants GA (EIGA) with different immigrant mutation probabilities and proposes an adaptive mechanism that tends to improve the performance in DOPs. Our experimental study shows that the proposed adaptive immigrants GA outperforms EIGA in almost all dynamic test cases and avoids the tedious work of finetuning the immigrant mutation probability parameter.Evolutionary Computation (CEC), 2013 IEEE Congress on; 01/2013  [Show abstract] [Hide abstract]
ABSTRACT: In recent years, there has been a growing interest in applying genetic algorithms to dynamic optimization problems. In this study, we present an extensive performance evaluation and comparison of 13 leading evolutionary algorithms with different characteristics on a common platform by using the moving peaks benchmark and by varying a set of problem parameters including shift length, change frequency, correlation value and number of peaks in the landscape. In order to compare solution quality or the efficiency of algorithms, the results are reported in terms of both offline error metric and dissimilarity factor, our novel comparison metric presented in this paper, which is based on signal similarity. Computational effort of each algorithm is reported in terms of average number of fitness evaluations and the average execution time. Our experimental evaluation indicates that the hybrid methods outperform the related work with respect to quality of solutions for various parameters of the given benchmark problem. Specifically, hybrid methods provide up to 24% improvement with respect to offline error and up to 30% improvement with respect to dissimilarity factor by requiring more computational effort than other methods.Applied Intelligence 07/2012; 37(1). · 1.85 Impact Factor
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