The influence of population genetics for the redesign of genetic algorithms

• Autorzy: Affenzeller M. , Wagner S.
• Języki publikacji: EN

Abstrakty

EN

This contribution considers recent results of population genetics in order to present generic extensions to the general concept of a Genetic Algorithm (GA). Consequently a new model for self-adaptive selection pressure steering is presented (Offspring Selection), taking advantage of the interplay between directed genetic drift and selection, resulting in a new class of Genetic Algorithms. As a result, we introduce and empirically analyze the generic extensions to the general GA concept, which make genetic search more stable in terms of operators, and allows steering and scaling up of global solution quality to the highest quality regions without using problem specific information or local searches.

Słowa kluczowe

EN

genetic algorithms; optimisation techniques; directed genetic drift; self-adaptive selection pressure steering

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Systems Science
• Rocznik: 2004
• Tom: Vol. 30, no 4
• Strony: 41--49
• Opis fizyczny: Bibliogr. 12 poz., wykr.

Twórcy

autor

Affenzeller M.

• Institute of Systems Theory and Simulation, Johannes Kepler University, Altenbergerstrasse 69, A-4040 Linz, Austria

autor

Wagner S.

• Institute of Systems Theory and Simulation, Johannes Kepler University, Altenbergerstrasse 69, A-4040 Linz, Austria

Bibliografia

• [1] Affenzeller M., Wagner S., SASEGASA: An Evolutionary Algorithm for Retarding Premature Convergence by Self-Adaptive Selection Pressure Steering, Computational Methods in Neural Modeling, Springer LNCS, Vol. 2686, 2003, 438^45.
• [2] Affenzeller M., Wagner S., SASEGASA: A New Generic Parallel Evolutionary Algorithm for Achieving Highest Quality Results, Journal of Heuristics - Special Issue on New Advances on Parallel Meta-Heuristics for Complex Problems, Kluwer Academic Publishers, Vol. 10, 2004, 239-263.
• [3] Braune R., Wagner S., Affenzeller M., Applying Genetic Algorithms to the Optimization of Production Planning in a Real-World Manufacturing Environment, Cybernetics and Systems 2004, Austrian Society for Cybernetic Studies, 2004, 41-46.
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• [11] Winkler S., Affenzeller M., Wagner S., Identifying Nonlinear Model Structures Using Genetic Programming Techniques, Cybernetics and Systems 2004, Austrian Society for Cybernetic Studies, 2004, 689-694.
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