Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
479290 | European Journal of Operational Research | 2007 | 12 Pages |
Abstract
In this article, a new framework for evolutionary algorithms for approximating the efficient set of a multiobjective optimization (MOO) problem with continuous variables is presented. The algorithm is based on populations of variable size and exploits new elite preserving rules for selecting alternatives generated by mutation and recombination. Together with additional assumptions on the considered MOO problem and further specifications on the algorithm, theoretical results on the approximation quality such as convergence in probability and almost sure convergence are derived.
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Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Thomas Hanne,