Designing efficient master-slave parallel genetic algorithms. 12pp.
TR No.: 97004 | Download PDF | Download PS
Abstract:
A simple technique to reduce the execution time of genetic algorithms (GAs) is to divide the task of evaluating the population among several processors. This class of algorithms is called “global” parallel GAs because selection and mating consider the entire population. Global parallel GAs are usually implemented as master-slave programs and require constant interprocessor communication. This will affect their performance, but most investigations of these algorithms ignore the penalty caused by communications. This paper presents an analysis of the execution time of global parallel GAs that includes a simple model of the time used in communications and shows that there is an optimal number of processors that minimizes the execution time. To further reduce the execution time we recommend the use of hybrids that combine global and coarse-grained parallel GAs.
Posted: January 20th, 1997 under Genetic algorithms.
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