A Step Size Preserving Directed Mutation Operator

Berlik, Stefan

doi:10.1007/978-3-540-24854-5_79

Cited by 6 publications

(4 citation statements)

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“…Asymmetric mutation: the skewness of the mutation distribution is controlled self-adaptively in the asymmetric mutation approach by Hildebrandt and Berlik [11,35]. Although one of the classical assumptions for mutation operators is their unbiasedness [15], asymmetric mutation turns out to be advantageous on some problem classes, e.g., ridge functions.…”

Section: Continuous Mutation Strengthsmentioning

confidence: 99%

“…No significant superiority of any of the two 1-point variants can be reported in mean, but the self-adaptive variants achieve the best results 10 OneMax is a pseudo-binary function. For a bit string x2f0; 1g N , maximize f ðxÞ :¼ P N i¼1 x i with x 2 f0; 1g N optimum x * = (1, ..., 1) with f(x * ) = N. 11 3-SAT is a propositional satisfiability problem, each clause contains k = 3 literals; we randomly generate formulas with N = 50 variables and 150 clauses; the fitness function is: #of true clauses #of all clauses :…”

Section: Crossover Pointsmentioning

confidence: 99%

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Evolutionary self-adaptation: a survey of operators and strategy parameters

Krämer

2010

Evol. Intel.

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The success of evolutionary search depends on adequate parameter settings. Ill conditioned strategy parameters decrease the success probabilities of genetic operators. Proper settings may change during the optimization process. The question arises if adequate settings can be found automatically during the optimization process. Evolution strategies gave an answer to the online parameter control problem decades ago: self-adaptation. Self-adaptation is the implicit search in the space of strategy parameters. The self-adaptive control of mutation strengths in evolution strategies turned out to be exceptionally successful. Nevertheless, for years self-adaptation has not achieved the attention it deserves. This paper is a survey of self-adaptive parameter control in evolutionary computation. It classifies self-adaptation in the taxonomy of parameter setting techniques, gives an overview of automatic online-controllable evolutionary operators and provides a coherent view on search techniques in the space of strategy parameters. Beyer and Sendhoff's covariance matrix self-adaptation evolution strategy is reviewed as a successful example for self-adaptation and exemplarily tested for various concepts that are discussed.

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Section: Continuous Mutation Strengthsmentioning

confidence: 99%

Section: Crossover Pointsmentioning

confidence: 99%

Evolutionary self-adaptation: a survey of operators and strategy parameters

Krämer

2010

Evol. Intel.

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“…The highest probability to reproduce mutations is still in the neighborhood environment of the child. In order to decouple asymmetry from the step sizes a normalized directed mutation operator has recently been proposed by Berlik [3].…”

Section: Directed Mutationmentioning

confidence: 99%

“…(i (0, 1), ..., IAN(O, 1)) (2) where Ali(0, 1) provides a random number based on a Gaussian distribution with expected value 0 and standard deviation 1. The strategy variable itself is mutated with the lognormal rule: := Oe((rA(0,1)) (3) This mechanism is the key to self-adaptation of the step sizes.…”

Section: Mutation Operators For Evolution Strategiesmentioning

confidence: 99%

A New Mutation Operator for Evolution Strategies for Constrained Problems

Krämer

Ting

Büning³

2005 IEEE Congress on Evolutionary Computation

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We propose a new mutation operator -the biased mutation operator (BMO) -for evolution strategies, which is capable of handling problems for constrained fitness landscapes. The idea of our approach is to bias the mutation ellipsoid in relation to the parent and therefore lead the mutations into a beneficial direction self-adaptively. This helps to improve the success rate to reproduce better offspring. Experimental results show this bias enhances the solution quality within constrained search domains. The number of the additional strategy parameters used in our approach equals to the number of dimensions of the problem. Compared to the correlated mutation, the BMO needs much less memory and supersedes the computation of the rotation matrix of the correlated mutation and the asymmetric probability density function of the directed mutation.

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Intragenerational Mutation Shape Adaptation

Berlik

Reusch

Computational Intelligence, Theory and Applications

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A Step Size Preserving Directed Mutation Operator

Cited by 6 publications

References 0 publications

Evolutionary self-adaptation: a survey of operators and strategy parameters

Evolutionary self-adaptation: a survey of operators and strategy parameters

A New Mutation Operator for Evolution Strategies for Constrained Problems

Intragenerational Mutation Shape Adaptation

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