The influence of different parallel genetic algorithm (PGA) architectures on the GA convergence properties is analysed. Next, two proposed versions of these PGA architectures are compared – homogenous and heterogeneous. Finally the effect of re-initialisation in some partial populations on the PGA convergence has been analysed. The proposed PGA modifications are useful mainly in case of non-smo...

Parallel genetic algorithms (GAs) are complex programs that are controlled by many parameters, which aaect their search quality and their eeciency. The goal of this paper is to provide guidelines to choose those parameters rationally. The investigation centers on the sizing of populations, because previous studies show that there is a crucial relation between solution quality and population siz...

Over the last years, interest in hybrid metaheuristics has risen considerably in the field of optimization. Combinations of algorithms such as genetic algorithms (GAs) and local search (LS) methods have provided very powerful search algorithms. However, due to their complexity, the computational time of the solution search exploration remains exorbitant when large problem instances are to be so...

In this paper several design methods for parallel genetic algorithms (PGAs) optimizing morphological lters are discussed and an optimal scale and machine independent PGA for a loosely coupled, homogeneous or inhomogeneous multiprocessor computer is developed. The optimization is made in terms of computation speed, parallelization eeciency and quality. Quality means, in this context, the tness o...

Parallel genetic algorithms are often very different from the \traditional" genetic algorithm proposed by Holland, especially with regards to population structure and selection mechanisms. In this paper we compare several parallel genetic algorithms across a wide range of optimization functions in an attempt to determine whether these changes have positive or negative impact on their problem-so...

In this paper we address the physical parallelization of a very efficient genetic algorithm (GA) known as gradual distributed real-coded GA (GD-RCGA). This search model naturally provides a set of eight subpopulations residing in a cube topology having two faces for promoting exploration and exploitation. The resulting technique has been shown to yield very accurate results in continuous optimi...

As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use better algorithms and more eecient implementations to reach good solutions fast. This chapter describes the implementation of master-slave and multiple-population parallel GAs. The goal of the chapter is to help others to implement their own parallel codes. To this eeect, the text discusses some of th...

This paper describes a parallel genetic algorithm which is linked to CFD analysis for the design of optimal airfoils. The method has been implemented on a variety of parallel architec-tures, and results to illustrate its application are presented. A common problem with genetic algorithms (or GAs) is how to maintain diversity of the gene pool and avoid premature convergence of the population. Su...