• Journal of the Institute of Convergence Signal Processing
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• v.7 no.4
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• pp.201-206
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• 2006

Genetic Algorithms have been known to provide near optimal solutions for various optimization problems in engineering. In this paper, we study the effect of gene order in genetic algorithms on the defining length of the schema with high fitness values. Its effect on the performance of genetic algorithms was also analyzed through two well known problems. A few gene reordering methods were proposed for graph partitioning and knapsack problems. Experimental results showed that genetic algorithms with gene reordering could find solutions of better qualities compared to the ones without gene reordering. It is very important to find proper reordering method for a given problem to improve the performance of genetic algorithms.

• Journal of KIISE:Software and Applications
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• v.32 no.10
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• pp.974-983
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• 2005

In genetic algorithms with lotus-based encoding, static gene reordering is to locate the highly related genes closely together. It helps the genetic algorithms to create and preserve the schema of high-quality effectively. In this paper, we propose a static reordering framework for linear locus-based encoding. It differs from existing reorderings in that it is independent of problem-specific knowledge. It makes a complete graph where weights represent the interelationship between each pair of genes. And, it transforms the graph into a unweighted sparse graph by choosing the edges having relatively high weight. It finds a gene reordering by graph search method. Through the wide experiments about several problems, the method proposed in this paper shows significant performance improvement as compared with the genetic algorithm that does not rearrange genes.