GA-Optimal fitness functions

descriptionPublicationkeyboard_double_arrow_right Part of book or chapter of book , Conference object , Article 01 Jan 1998Publisher:Springer Berlin Heidelberg

Authors: Joseph L. Breeden;

doi: 10.1007/bfb0040763

GA-Optimal fitness functions

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Abstract

Genetic algorihtms (GA) are supposed to suceed through the use of ‘implicit parallelism’ and ‘building blocks’. Given these properties, we can create rules for constructing GA-optimal fitness functions. Some of these rules are also relevant to evolutionary programming and evolution strategies searches. The goal is to help the practitioner develop an intuition about when GA are effective.

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