Fitness Variance of Formae and Performance Prediction

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Fitness Variance of Formae and Performance Prediction (1994)

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by Nicholas J. Radcliffe , Patrick D. Surry , Eh Jz , Eh Jz

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66 - 7 self

BibTeX

@MISC{Radcliffe94fitnessvariance,
    author = {Nicholas J. Radcliffe and Patrick D. Surry and Eh Jz and Eh Jz},
    title = {Fitness Variance of Formae and Performance Prediction},
    year = {1994}
}

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Abstract

Representation is widely recognised as a key determinant of performance in evolutionary computation. The development of families of representation-independentoperators allows the formulation of formal representation-independent evolutionary algorithms. These formal algorithms can be instantiated for particular search problems by selecting a suitable representation. The performance of different representations, in the context of any given formal representation-independent algorithm, can then be measured. Simple analyses suggest that fitness variance of formae (generalised schemata) for the chosen representation might act as a performance predictor for evolutionary algorithms. This hypothesis is tested and supported through studies of four different representations for the travelling sales-rep problem (TSP) in the context of both formal representation-independentgenetic algorithms and corresponding memetic algorithms. 1 Motivation The subject of this paper is representation i...

Keyphrases

fitness variance performance prediction different representation formal representation-independent algorithm formal representation-independentgenetic algorithm particular search problem performance predictor key determinant evolutionary computation formal algorithm chosen representation memetic algorithm formal representation-independent evolutionary algorithm evolutionary algorithm simple analysis suitable representation sales-rep problem

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