Derivative operators for preference predicate evolution

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This work deals with the problem of function learning by genetic algorithms where the function is used as a preference predicate. In such a case, learning the exact function is not necessary since any function that preserves the order induced by the target function is sufficient. The paper presents a methodology for solving the problem with genetic algorithms. We first consider the representation issues involved in learning such a function, and conclude that canonical representation, relative coding, and search restrictions, are required. We then show that the traditional homologous genetic operators are not appropriate for such learning, and introduce a new configurable analogous genetic operator, named derivative crossover. This operator works on the derivative of the chromosomes and is therefore suitable for preference predicate learning where only the relative values of the functions are important. We support our methodology by a set of experiments performed in the domain of continuous function learning and in the domain of evaluation-function learning for game-playing. The experiments show that indeed using derivative operators increases the speed of learning significantly.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Parallel Problem Solving from Nature (PPSN III)
EditorsYuval Davidor, Hans-Paul Schwefel, Reinhard Männer
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783540584841
StatePublished - 1994
Externally publishedYes
Event3rd International Conference on Parallel Problem Solving from Nature, PPSN III 1994 - Jerusalem, Israel
Duration: 9 Oct 199414 Oct 1994

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume866 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Conference on Parallel Problem Solving from Nature, PPSN III 1994


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