Parallel Genetic Algorithms on Distributed-Memory Architectures

• Authors: Ricardo Bianchini;Christopher M. Brown
• Venue: Parallel Genetic Algorithms on Distributed-Memory Architectures
• Year: 1993

The implementation of parallel genetic algorithms raises many important issues. These issues can be divided into two main classes: genetic search quality and execution performance. In the context of parallel genetic algorithms on distributed-memory computers, performance considerations have always driven the design of implementations. Thus, centralized implementations have almost always been excluded from any consideration for distributed-memory architectures. .pp The work we present here defines a set of genetic algorithm implementation alternatives for distributed-memory computers, in which strategies with some centralization are included. Each of our implementation alternatives uses a different level of distribution of the population, from the single logically centralized population to a totally distributed set of subpopulations. .pp The design alternatives we define can be applied to the implementation of any parallel genetic algorithm. As an example of such an implementation, we study the quality of the search and the execution performance of our strategies on the 0-1 Integer Linear Programming problem, on a Transputer network. Our results show that implementations incurring higher overheads can produce as good or better solutions faster than than very "efficient" implementations, depending on the characteristics of the problem at hand. More specifically, in some cases, utilizing more centralized parallel genetic search strategies results in the fastest convergence towards the optimal solution, therefore reducing the number of generations needed by the algorithm.