Optimization and Evolutionary Search: Related Issues

Maumita Bhattacharya
Charles Sturt University, Australia

Ladda ner artikelhttp://www.ep.liu.se/ecp_article/index.en.aspx?issue=014;article=007

Ingår i: Nordic MPS 2004. The Ninth Meeting of the Nordic Section of the Mathematical Programming Society

Linköping Electronic Conference Proceedings 14:7, s.

Visa mer +

Publicerad: 2004-12-28


ISSN: 1650-3686 (tryckt), 1650-3740 (online)


Evolutionary algorithms (EA) have been long accepted as efficient global optimizers. Given a search space S and an objective function g defined on it; the problem is to find the global maximum (or minimum) of g in S. To apply EA’s heuristic search; the coding function or representation ? is created; that partially maps S to the finite chromosome space C. The genetic operators are used to create new solutions such that Cn ? Cm.

However; as the evolutionary search progresses; it is important to avoid reaching a state where the genetic operators can no longer produce superior offspring; prematurely. This is likely to occur when the search space reaches a homogeneous or near-homogeneous configuration converging to a local optimal solution. Maintaining a certain degree of population diversity is widely believed to help curb this problem. This paper discusses the problem of premature convergence related to EA based optimization. A novel technique is presented; that uses informed genetic operations to reach promising; but un/under-explored areas of the search space; while discouraging local convergence; to curb premature convergence. Elitism is used at a different level aiming at convergence. The proposed technique’s improved performance in terms solution precision and convergence characteristics is observed on a number of benchmark test functions with a genetic algorithm (GA) implementation.


Inga nyckelord är tillgängliga


Inga referenser tillgängliga

Citeringar i Crossref