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  1. Proceedings of the 9th international conference on Foundations of genetic algorithms (FOGA'07)
  2. Decomposition of fitness functions in random heuristic search
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Inbreeding properties of geometric crossover and non-geometric recombinations
Just what are building blocks?
Sufficient conditions for coarse-graining evolutionary dynamics
On the brittleness of evolutionary algorithms
Mutative self-adaptation on the sharp and parabolic ridge
Genericity of the fixed point set for the infinite population genetic algorithm
Neighborhood graphs and symmetric genetic operators
Decomposition of fitness functions in random heuristic search
On the effects of bit-wise neutrality on fitness distance correlation, phenotypic mutation rates and problem hardness
Continuous optimisation theory made easy? finite-element models of evolutionary strategies, genetic algorithms and particle swarm optimizers
Saddles and barrier in landscapes of generalized search operators

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Decomposition of fitness functions in random heuristic search

Content Provider ACM Digital Library
Author Borenstein, Yossi Poli, Riccardo
Abstract We show that a fitness function, when taken together with an algorithm, can be reformulated as a set of probability distributions. This set can, in some cases, be equivalently viewed as an information vector which gives ordering information about pairs of search points in the domain. Certain performance criteria definable over such an information vector can be learned by linear regression in such a way that extrapolations can sometimes be made: the regression can make performance predictions about functions it has not seen. In addition, the vector can be taken as a model of the fitness function and used to compute features of it like difficultly via vector calculations.
Starting Page 123
Ending Page 137
Page Count 15
ISBN 9783540734796
ISSN 03029743
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2007-01-08
Publisher Place Berlin, Heidelberg
Access Restriction Subscribed
Content Type Text
Resource Type Article
Subject Theoretical Computer Science Computer Science
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