Hypothesis-test based genetic algorithm for stochastic optimization problems
Abstract：To effectively solve the stochastic optimization problems with non-deterministic and multi-modal properties, a class of hypothesis-test based genetic algorithm is proposed. The algorithm performs reasonable estimation by multiple evaluations, searches the design space effectively via genetic operators, and enhances the searching ability and population diversity by hypothesis test to overcome premature convergence. Based on typical stochastic functional and combinatorial optimization problems, the effects of hypothesis test, performance estimation number and magnitude of noise on the performance of the approach are studied, and the effectiveness and robustness of the proposed approach are demonstrated.
Keywords：genetic algorithm(GA)stochastic optimizationhypothesis test
Publication Date：2004-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 885-889 )