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The MaxSolve algorithm for coevolution
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Proceedings of the 2005 conference on Genetic and evolutionary computation table of contents
Washington DC, USA
SESSION: Coevolution table of contents
Pages: 483 - 489  
Year of Publication: 2005
ISBN:1-59593-010-8
Author
Edwin de Jong  Utrecht University, Utrecht, The Netherlands
Sponsors
SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 3,   Downloads (12 Months): 37,   Citation Count: 11
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ABSTRACT

Coevolution can be used to adaptively choose the tests used for evaluating candidate solutions. A long-standing question is how this dynamic setup may be organized to yield reliable search methods. Reliability can only be considered in connection with a particular solution concept specifying what constitutes a solution. Recently, monotonic coevolution algorithms have been proposed for several solution concepts. Here, we introduce a new algorithm that guarantees monotonicity for the solution concept of maximizing the expected utility of a candidate solution. The method, called MaxSolve, is compared to the IPCA algorithm and found to perform more efficiently for a range of parameter values on an abstract test problem.


REFERENCES

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