Genetic algorithms (GAs) have been extensively used in different domains as a means of
doing global optimization in a simple yet reliable manner. They have a much better chance …

Genetic Algorithms (GAs), an evolutionary technique which exhibits robustness under a
variety of problem environments have been the subject of increased interest for the past …

In this paper we describe a method for improving genetic-algorithm-based optimization
using search control. The idea is to utilize the sequence of points explored during a search …

A new technique called rank based crossover (RBC) to improve the speed of reaching
optimal solutions is introduced for genetic algorithms (GAs). In real life, marriages …

The genetic algorithm (GA) has been receiving increasing use as a global search and
optimization methodology, 1 and GA applications now extend to aerospace optimization …

Evolution strategies and gradient strategies are two different approaches to continuous
optimization. Salomon's evolutionary gradient search procedure is a hybrid strategy that …

Intensive studies of genetic algorithms (GAs) make the GAs really effective techniques
applicable to hard optimization problems. These studies suggest two key points in designing …

This paper introduces the notion of using coevolution to adapt the penalty factors of a fitness
function incorporated in a genetic algorithm for numerical optimization. The proposed …

Classical gradient methods and evolutionary algorithms represent two very different classes
of optimization techniques that seem to have very different properties. This paper discusses …

Genetic algorithms (GAs) have been shown to be quite effective at solving a wide range of
difficult problems. They are very efficient at exploring the entire search space; however, they …