Are evolutionary algorithms improved by large mutations?

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1141, Issue , 1996, Pages 347-355

  Kappler, C ^a  

^a HUMBOLDT UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; EVOLUTIONARY ALGORITHMS; GAUSSIAN DISTRIBUTION; OPTIMIZATION;

CAUCHY DISTRIBUTION; GAUSSIANS; HIGHER DIMENSIONS; LOCAL EXTREMUM; ONE DIMENSION; SADDLE POINT; SEARCH SPACES; TWO-DIMENSIONAL PROBLEM;

PROBLEM SOLVING;

EID: 0346291084     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-61723-X_999     Document Type: Conference Paper

References (10)

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2. I. Rechenberg, “Evolutionsstrategie’94”, Vol. 1 of Werkstatt Bionik und Evolution- stechnik, Frommann-Holzboog, Stuttgart (1994)
3. H.-P. Schwefel, “Evolution and optimum seeking”, John Wiley k, Sons Inc. (1995)
4. J.-P. Bouchard and A. Georges, “Anomalous diffusion in disordered media: statistical mechanics, models and physical applications”, Phys. Rep. 195, 127 (1990)
5. H. Szu and R. Hartley, “Fast simulated annealing”, Phys. Lett. A 122, 157 (1987)
6. L. Ingber “Very fast simulated re-annealing”, J. Math. Comp. Modelling 12, 967 (1989)
7. An evolutionary algorithm without cross over is characterized by (μ+, λ), where μ is the number of parents, and λ the number of children in each generation. In a {μ, μ) strategy, the λ parents of the next generation are the μ best of the λ children. In a (μ + λ) strategy, the parents of the next generation are the μ best of both parents and children of the last generation. See also Refs. [1, 2, 3].
8. C. W. Gardiner “Handbook of stochastic methods”, 2nd ed., Springer (1994)
9. M. Conrad, W. Ebeling “M. V. Volkenstein, evolutionary thinking and the structure of fitness landscapes”, BioSystems 27, 125 (1992)
10. Z. Michalewicz “Genetic Algorithms + Data Structures = Evolution Programs”, Springer (1992).