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Competitive evolution: A natural approach to operator selection

  • Q. Tuan Pham
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 956)

Abstract

One of the main problems in applying evolutionary optimisation methods is the choice of operators and parameter values. This paper propose a competitive evolution method, in which several subpopulations are allowed to compete for computer time. The population with the fittest members, and that with the highest improvement rate in the recent past, are rewarded.

When using identical strategies in the subpopulations, this competitive strategy provides an insurance against unlucky runs while extracting only an insignificant cost in terms of extra function evaluations. When using different strategies in the subpopulations, it ensures that the best strategies are used and again the extra cost is not great. Competitive evolution is at its best when an operator — or the lack of it — may have a very detrimental effect which is not known in advance. Occasional mixing of the best performing subpopulations leads to further improvement.

Symbols

d

normalised distance between two vectors x (equation 2)

f(x)

function to be optimised

m

mutation frequency

mo

base mutation frequency

N

population size

Neval

number of fitness evaluations

Nvar

number of search variables

pux

uniform crossovers/total crossovers

R

weighting factor indicating bias of offspring towards fitter parent

S

standard deviation of offspring from expected value, as a fraction of δ

x=(x1, x2...)

vector of search variables

xi,min

minimum allowable value of xi

xi,max

maximum allowable value of xi

δ

distance for which the exponential part of the mutation frequency is 0.5

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Q. Tuan Pham
    • 1
  1. 1.School of Chemical Engineering and Industrial ChemistryUniversity of New South WalesSydneyAustralia

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