Implementing Fast and Flexible Parallel Genetic Algorithms

As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use better algorithms and more efficient 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 effect, the text discusses some of the design decisions that were made and possible improvements to the code. 1 Introduction Genetic algorithms (GAs) are making their way from universities and research centers into commercial and industrial settings. In both academia and industry, genetic algorithms are being used to find solutions to harder problems, and it is becoming necessary to use improved algorithms and faster implementations to obtain good solutions in reasonable amounts of time. Fortunately, parallel computers are making a similar move into industry, and GAs are very suitable to be implemented on parallel platforms. The..