Penalty Function Methods for Constrained Optimization with Genetic Algorithms

Penalty Function Methods for Constrained Optimization with Genetic Algorithms: A Statistical Analysis

Genetic algorithms (GAs) have been successfully applied to numerical optimization problems. Since GAs are usually designed for unconstrained optimization, they have to be adapted to tackle the constrained cases, i.e. those in which not all representable solutions are valid. In this work we experimentally compare 5 ways to attain such adaptation. Our analysis relies on the usual method of select...

Adaptive Penalty Methods for Genetic Optimization of Constrained Combinatorial Problems

Penalty and Barrier Methods for Constrained Optimization

Fuzzy Penalty Function Approach for Constrained Function Optimization with Evolutionary Algorithms

In this paper, a novel fuzzy penalty function approach is proposed for solving the constrained optimization problems using evolutionary algorithms. The fuzzy penalty is constructed according to the information contained in individuals so that it can be tuned to reflect the appropriate penalty need for better search. Simulations on ten case studies indicate that the proposed method is very effec...

Constrained Evolutionary Optimization - the penalty function approach