혼합 유전알고리즘을 이용한 비선형 최적화문제의 효율적 해법

This paper describes the applications of genetic algorithm to nonlinear constrained optimization problems. Genetic algorithms are combinatorial in nature, and therefore are computationally suitable for treating continuous and idstrete integer design variables. For several problems , the conventional genetic algorithms are ill-defined , which comes from the application of penalty function , encoding and decoding methods, fitness scaling, and premature convergence of solution. Thus, we develope a hybrid genetic algorithm to resolve these problems and present two examples to demonstrate the effectiveness of the methodology developed in this paper.