Abstract

Evolutionary computation techniques have received a lot of attention regarding their potential as optimization techniques for complex numerical functions. However, they have not produced a significant breakthrough in the area of nonlinear programming due to the fact that they have not addressed the issue of constraints in a systematic way. Only recently several methods have been proposed for handling nonlinear constraints by evolutionary algorithms for numerical optimization problems; however, these methods have several drawbacks and the experimental results on many test cases have been disappointing. In this paper we (1) discuss difficulties connected with solving the general nonlinear programming problem, (2) survey several approaches which have emerged in the evolutionary computation community, and (3) provide a set of eleven interesting test cases, which may serve as a handy reference for future methods. 1 Introduction The general nonlinear programming (NLP) problem is to find ~x ...

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