EA models and population fixed-points versus mutation rates for functions of unitation | Zendy

J. Neal Richter | Zendy; John Paxton | Zendy; Alden H. Wright | Zendy

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EA models and population fixed-points versus mutation rates for functions of unitation

Author(s) -

J. Neal Richter,

John Paxton,

Alden H. Wright

Publication year - 2005

Publication title -

citeseer x (the pennsylvania state university)

Language(s) - English

Resource type - Conference proceedings

ISBN - 1-59593-010-8

DOI - 10.1145/1068009.1068211

Subject(s) - fixed point , crossover , equivalence (formal languages) , computation , genetic algorithm , simple (philosophy) , population , mathematics , mutation , mathematical optimization , computer science , algorithm , discrete mathematics , artificial intelligence , mathematical analysis , biology , demography , sociology , gene , philosophy , biochemistry , epistemology

Using a dynamic systems model for the Simple Genetic Algorithm due to Vose[1], we analyze the fixed point behavior of the model without crossover applied to functions of unitation. Unitation functions are simplified fitness functions that reduce the search space into a smaller number of equivalence classes. This reduction allows easier computation of fixed points. We also create a dynamic systems model from a simple nondecreasing EA like the (1+1) EA and variants, then analyze this models on unitation classes.

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