No abstract

International audienceOver the years, several meta-heuristic algorithms were proposed and are now emerging as common methods for constrained optimization problems. Among them, genetic algorithms (GA's) shine as popular evolutionary algorithms (EA's) in engineering optimization. Most engineering design problems are difficult to resolve with conventional optimization algorithms because they are highly nonlinear and contain constraints. In order to handle these constraints, the most common technique is to apply penalty functions. The major drawback is that they require tuning of parameters, which can be very challenging. In this paper, we present a constraint-handling technique for GA's solely using the violation factor, called VCH (Violation Constraint-Handling) method. Several benchmark problems from the literature are examined. The VCH technique was able to provide a consistent performance and match results from other GA-based techniques

This article presents a newly proposed selection process for genetic algorithms on a class of unconstrained optimization problems. The k-means genetic algorithm selection process (KGA) is composed of four essential stages: clustering, membership phase, fitness scaling and selection. Inspired from the hypothesis that clustering the population helps to preserve a selection pressure throughout the evolution of the population, a membership probability index is assigned to each individual following the clustering phase. Fitness scaling converts the membership scores in a range suitable for the selection function which selects the parents of the next generation. Two versions of the KGA process are presented: using a fixed number of clusters K (KGA f ) and via an optimal partitioning K opt (KGA o ) determined by two different internal validity indices. The performance of each method is tested on seven benchmark problems.