Evolutionary Programming Using A Mixed Strategy with Incomplete Information

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dc.contributor.author Shen, Liang
dc.contributor.author He, Jun
dc.date.accessioned 2010-09-24T12:16:28Z
dc.date.available 2010-09-24T12:16:28Z
dc.date.issued 2010-09-24
dc.identifier.citation Shen , L & He , J 2010 , ' Evolutionary Programming Using A Mixed Strategy with Incomplete Information ' . en
dc.identifier.other PURE: 160197
dc.identifier.other dspace: 2160/5713
dc.identifier.uri http://hdl.handle.net/2160/5713
dc.description L. Shen and J. He. Evolutionary Programming Using A Mixed Strategy with Incomplete Information. Proceedings of the 2010 UK Workshop on Computational Intelligence, 2010. en
dc.description.abstract Evolutionary Programming (EP) has been modified in various ways. In particular, modifications of the mutation operator have been proved to be capable of significantly improving the performance of EP. However, while each of proposed mutation operators (e.g. Gaussian mutation and Cauchy mutation) may be suitable for solving certain types of problem, none of them are suitable for all problems. Mixed strategies have therefore been proposed in order to combine the advantages of different operators. The design of a mixed strategy is currently based on the premise that complete and perfect information is held for each mutation operator in the mixed strategy such that the payoff functions to each pure strategy are common knowledge. This paper presents a modified mixed strategy (IMEP) involving a process with incomplete information. Experimental results show that IMEP outperforms pure strategy algorithms in spite of the lack of information. The experiments also show that the results are similar to those generated by the original algorithm, which was complete information. en
dc.language.iso eng
dc.title Evolutionary Programming Using A Mixed Strategy with Incomplete Information en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.description.status Non peer reviewed en


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