%0 Journal Article %1 srinivas1994adaptive-probab %A Srinivas, M. %A Patnaik, Lalit M. %D 1994 %J IEEE Transactions on Systems, Man, and Cybernetics %K control crossover, meta-ga mutation, parameter %N 4 %P 656-667 %T Adaptive probabilities of crossover and mutation in genetic algorithms %V 24 %X In this paper we describe an efficient approach for solving the economic dispatch problem using Genetic Algorithms (GAs). We recommend the use of adaptive probabilities crossover and mutation to realize the twin goals of maintaining diversity in the population and sustaining the convergence capacity of the GA. In the Adaptive Genetic Algorithm (AGA), the probabilities of crossover and mutation, pc and pm, are varied depending on the fitness values of the solutions. By using adaptively varying, pc and pm, we also provide a solution to the problem of deciding the optimal values of pc and pm, i.e., pc and pm need not be specified at all. We compare the performance of the AGA with that of the Standard GA (SGA) in optimizing the penalty function by using a sequential unconstrained minimization technique (SUMT). In this work, the AGA has been applied to a practical 14-bus system to show its feasibility and capabilities. The Numerical and graphical results show that the proposed approach is faster and more robust than the simple static Genetic Algorithm.

@article{srinivas1994adaptive-probab, abstract = {In this paper we describe an efficient approach for solving the economic dispatch problem using Genetic Algorithms (GAs). We recommend the use of adaptive probabilities crossover and mutation to realize the twin goals of maintaining diversity in the population and sustaining the convergence capacity of the GA. In the Adaptive Genetic Algorithm (AGA), the probabilities of crossover and mutation, pc and pm, are varied depending on the fitness values of the solutions. By using adaptively varying, pc and pm, we also provide a solution to the problem of deciding the optimal values of pc and pm, i.e., pc and pm need not be specified at all. We compare the performance of the AGA with that of the Standard GA (SGA) in optimizing the penalty function by using a sequential unconstrained minimization technique (SUMT). In this work, the AGA has been applied to a practical 14-bus system to show its feasibility and capabilities. The Numerical and graphical results show that the proposed approach is faster and more robust than the simple static Genetic Algorithm. }, added-at = {2009-04-07T10:59:16.000+0200}, author = {Srinivas, M. and Patnaik, Lalit M.}, bdsk-file-1 = {YnBsaXN0MDDUAQIDBAUGCQpYJHZlcnNpb25UJHRvcFkkYXJjaGl2ZXJYJG9iamVjdHMSAAGGoNE HCFRyb290gAFfEA9OU0tleWVkQXJjaGl2ZXKoCwwXGBkdJCVVJG51bGzTDQ4PEBEUViRjbGFzc1 dOUy5rZXlzWk5TLm9iamVjdHOAB6ISE4ACgAOiFRaABIAGWWFsaWFzRGF0YVxyZWxhdGl2ZVBhd GjSDRobHFdOUy5kYXRhgAVPEQH2AAAAAAH2AAIAAAxNYWNpbnRvc2ggSEQAAAAAAAAAAAAAAAAA AADD42C9SCsAAAANTSwfc3Jpbml2YXMtMTk5NC1hZGFwdGl2I0YxMUUyLnBkZgAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA8R4sOlA1wAAAAAAAAAAAABAAMAAAkgAAAAAAAAAAAAAA AAAAAAA3NyYwAAEAAIAADD41KtAAAAEQAIAADDpPVMAAAAAQAYAA1NLAANTRwADU0YAAqkLwAKp BgAAHrcAAIAWE1hY2ludG9zaCBIRDpVc2Vyczpzc3QyMTA6RG9jdW1lbnRzOlBhcGVyczpET1dO TE9BRFM6c3JjOnNyaW5pdmFzLTE5OTQtYWRhcHRpdiNGMTFFMi5wZGYADgBEACEAcwByAGkAbgB pAHYAYQBzAC0AMQA5ADkANAAtAGEAZABhAHAAdABpAHYAZQAtAHAAcgBvAGIAYQBiAC4AcABkAG YADwAaAAwATQBhAGMAaQBuAHQAbwBzAGgAIABIAEQAEgBNVXNlcnMvc3N0MjEwL0RvY3VtZW50c y9QYXBlcnMvRE9XTkxPQURTL3NyYy9zcmluaXZhcy0xOTk0LWFkYXB0aXZlLXByb2JhYi5wZGYA ABMAAS8AABUAAgAN//8AANIeHyAhWCRjbGFzc2VzWiRjbGFzc25hbWWjISIjXU5TTXV0YWJsZUR hdGFWTlNEYXRhWE5TT2JqZWN0XxAyLi4vRE9XTkxPQURTL3NyYy9zcmluaXZhcy0xOTk0LWFkYX B0aXZlLXByb2JhYi5wZGbSHh8mJ6InI1xOU0RpY3Rpb25hcnkACAARABoAHwApADIANwA6AD8AQ QBTAFwAYgBpAHAAeACDAIUAiACKAIwAjwCRAJMAnQCqAK8AtwC5ArMCuALBAswC0ALeAuUC7gMj AygDKwAAAAAAAAIBAAAAAAAAACgAAAAAAAAAAAAAAAAAAAM4}, bdsk-url-1 = {http://www.cs.vu.nl/ci/ec/catalog/src/srinivas-1994-adaptive-probab.pdf}, bibsource = {DBLP, http://dblp.uni-trier.de}, biburl = {https://www.bibsonomy.org/bibtex/2b77cd531dc6cf729e2b14cbfb3f39cf6/selmarsmit}, date-added = {2008-05-14 11:25:38 +0200}, date-modified = {2008-05-26 15:53:43 +0200}, description = {Selmar}, interhash = {01c77f31d986832f24687b483c794dac}, intrahash = {b77cd531dc6cf729e2b14cbfb3f39cf6}, journal = {IEEE Transactions on Systems, Man, and Cybernetics}, keywords = {control crossover, meta-ga mutation, parameter}, number = 4, pages = {656-667}, timestamp = {2009-04-07T10:59:19.000+0200}, title = {Adaptive probabilities of crossover and mutation in genetic algorithms}, volume = 24, year = 1994 }