Optimizing engineering designs using a combined genetic search

Abstract

In the optimization of engineering designs, traditional search and optimization methods face at least two difficulties: (i) since each is specialized in solving a particular type of problem, one method does not work well on different types of problems (ii) most of them are designed to work on continuous search spaces. Since different optimal engineering design problems give rise to objective and constraint functions of varying degree of nonlinearity and since most engineering design problems involve mixed variables (zero-one, discrete, and continuous), designers often face difficulty in using the traditional methods. In this paper, a combined genetic search technique (GeneAS) is suggested to solve mixed-integer programming problems often encountered in engineering design activities. GeneAS uses a combination of binary-coded and real-coded GAs to handle different types of variables.