%0 Journal Article %1 czarn2004statistical-exp %A Czarn, A. %A MacNish, C. %A Vijayan, K. %A Turlach, B.A. %A Gupta, R. %D 2004 %J IEEE Transactions on Evolutionary Computation %K anova crossover, interaction, meta-ga, parameter, significance suites, test testing, tuning, %N 4 %P 405-421 %T Statistical exploratory analysis of genetic algorithms %V 8 %X The traditional concept of a genetic algorithm (GA) is that of selection, crossover and mutation. However, a limited amount of data from the literature has suggested that the niche for the beneficial effect of crossover upon GA performance may be smaller than has traditionally been held. Based upon previous results on not-linear-separable problems we decided to explore this by comparing two test problem suites, one comprising non- rotated functions and the other comprising the same functions rotated by 45 degrees rendering them not-linear-separable. We find that for the difficult rotated functions the crossover operator was detrimental to the performance of the GA. We conjecture that what makes a problem difficult for the GA is complex and involves factors such as the degree of optimization at local minima due to crossover, the bias associated with the mutation operator and the Hamming Distances present in the individual problems due to the encoding. Finally, we tested our GA on a real world landscape minimization problem to see if the results obtained would match those from the difficult rotated functions. We find that they match and that the features which make certain of the test functions difficult are also present in the real world problem.

@article{czarn2004statistical-exp, abstract = {The traditional concept of a genetic algorithm (GA) is that of selection, crossover and mutation. However, a limited amount of data from the literature has suggested that the niche for the beneficial effect of crossover upon GA performance may be smaller than has traditionally been held. Based upon previous results on not-linear-separable problems we decided to explore this by comparing two test problem suites, one comprising non- rotated functions and the other comprising the same functions rotated by 45 degrees rendering them not-linear-separable. We find that for the difficult rotated functions the crossover operator was detrimental to the performance of the GA. We conjecture that what makes a problem difficult for the GA is complex and involves factors such as the degree of optimization at local minima due to crossover, the bias associated with the mutation operator and the Hamming Distances present in the individual problems due to the encoding. Finally, we tested our GA on a real world landscape minimization problem to see if the results obtained would match those from the difficult rotated functions. We find that they match and that the features which make certain of the test functions difficult are also present in the real world problem. }, added-at = {2009-04-07T10:59:16.000+0200}, author = {Czarn, A. and MacNish, C. and Vijayan, K. and Turlach, B.A. and Gupta, R.}, bdsk-file-1 = {YnBsaXN0MDDUAQIDBAUGCQpYJHZlcnNpb25UJHRvcFkkYXJjaGl2ZXJYJG9iamVjdHMSAAGGoNE HCFRyb290gAFfEA9OU0tleWVkQXJjaGl2ZXKoCwwXGBkdJCVVJG51bGzTDQ4PEBEUViRjbGFzc1 dOUy5rZXlzWk5TLm9iamVjdHOAB6ISE4ACgAOiFRaABIAGWWFsaWFzRGF0YVxyZWxhdGl2ZVBhd GjSDRobHFdOUy5kYXRhgAVPEQHsAAAAAAHsAAIAAAxNYWNpbnRvc2ggSEQAAAAAAAAAAAAAAAAA AADD42C9SCsAAAANTSweY3phcm4tMjAwNC1zdGF0aXN0aWNhbC1leHAucGRmAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA6t1cRGGu0AAAAAAAAAAAABAAMAAAkgAAAAAAAAAAAAAA AAAAAAA3NyYwAAEAAIAADD41KtAAAAEQAIAADERf7NAAAAAQAYAA1NLAANTRwADU0YAAqkLwAKp BgAAHrcAAIAV01hY2ludG9zaCBIRDpVc2Vyczpzc3QyMTA6RG9jdW1lbnRzOlBhcGVyczpET1dO TE9BRFM6c3JjOmN6YXJuLTIwMDQtc3RhdGlzdGljYWwtZXhwLnBkZgAADgA+AB4AYwB6AGEAcgB uAC0AMgAwADAANAAtAHMAdABhAHQAaQBzAHQAaQBjAGEAbAAtAGUAeABwAC4AcABkAGYADwAaAA wATQBhAGMAaQBuAHQAbwBzAGgAIABIAEQAEgBKVXNlcnMvc3N0MjEwL0RvY3VtZW50cy9QYXBlc nMvRE9XTkxPQURTL3NyYy9jemFybi0yMDA0LXN0YXRpc3RpY2FsLWV4cC5wZGYAEwABLwAAFQAC AA3//wAA0h4fICFYJGNsYXNzZXNaJGNsYXNzbmFtZaMhIiNdTlNNdXRhYmxlRGF0YVZOU0RhdGF YTlNPYmplY3RfEC8uLi9ET1dOTE9BRFMvc3JjL2N6YXJuLTIwMDQtc3RhdGlzdGljYWwtZXhwLn BkZtIeHyYnoicjXE5TRGljdGlvbmFyeQAIABEAGgAfACkAMgA3ADoAPwBBAFMAXABiAGkAcAB4A IMAhQCIAIoAjACPAJEAkwCdAKoArwC3ALkCqQKuArcCwgLGAtQC2wLkAxYDGwMeAAAAAAAAAgEA AAAAAAAAKAAAAAAAAAAAAAAAAAAAAys=}, bdsk-url-1 = {http://www.cs.vu.nl/ci/ec/catalog/src/czarn-2004-statistical-exp.pdf}, bibsource = {DBLP, http://dblp.uni-trier.de}, biburl = {https://www.bibsonomy.org/bibtex/24381b4dd9d4206bad4bc3d5d482bf094/selmarsmit}, date-added = {2008-05-06 12:08:23 +0200}, date-modified = {2008-05-22 15:27:59 +0200}, description = {Selmar}, ee = {http://dx.doi.org/10.1109/TEVC.2004.831262}, interhash = {e0db0970714a9a2d154a34b59e65a4eb}, intrahash = {4381b4dd9d4206bad4bc3d5d482bf094}, journal = {IEEE Transactions on Evolutionary Computation}, keywords = {anova crossover, interaction, meta-ga, parameter, significance suites, test testing, tuning,}, number = 4, pages = {405-421}, timestamp = {2009-04-07T10:59:20.000+0200}, title = {Statistical exploratory analysis of genetic algorithms}, volume = 8, year = 2004 }