Convergence analysis of a self-adaptive multi-objective evolutionary algorithm based on grids
Research output: Contribution to journal › Article
Colleges, School and Institutes
Evolutionary algorithms have been successfully applied to various multi-objective optimization problems. However, theoretical studies on multi-objective evolutionary algorithms, especially with self-adaption, are relatively scarce. This paper analyzes the convergence properties of a self-adaptive (mu + 1)-algorithm. The convergence of the algorithm is defined, and general convergence conditions are studied. Under these conditions, it is proven that the proposed self-adaptive (mu + 1)-algorithm converges in probability or almost surely to the Pareto-optimal front. (c) 2007 Elsevier B.V. All rights reserved.
|Number of pages||6|
|Journal||Information Processing Letters|
|Publication status||Published - 1 Nov 2007|
- evolutionary algorithms, multi-objective optimization, analysis of algorithms, convergence