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Genetic operators for combinatorial optimization in TSP and microarray gene ordering
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DOI：10.1007/s10489-006-0018-y.png)
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Abstract
En 中文
This paper deals with some new operators of genetic algorithms and[-27pc] demonstrates their effectiveness to the traveling salesman problem (TSP) and microarray gene ordering. The new operators developed are nearest fragment operator based on the concept of nearest neighbor heuristic, and a modified version of order crossover operator. While these result in faster convergence of Genetic Algorithm (GAs) in finding the optimal order of genes in microarray and cities in TSP, the nearest fragment operator can augment the search space quickly and thus obtain much better results compared to other heuristics. Appropriate number of fragments for the nearest fragment operator and appropriate substring length in terms of the number of cities/genes for the modified order crossover operator are determined systematically. Gene order provided by the proposed method is seen to be superior to other related methods based on GAs, neural networks and clustering in terms of biological scores computed using categorization of the genes.
Keywords:
microarray
gene analysis
data mining
biocomputing
evolutionary algorithm
soft computing
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