Design and Comparison of Two Web Service Based Frameworks for Parallel Evaluation of the Population in Genetic Algorithms

Abstract

Genetic algorithms are powerful techniques for optimization of complexsystems. These methods require a large number of evaluations of candidate solutionswhich take huge CPU time. This paper introduces two web service based frameworksfor parallel evaluation of the population in genetic algorithm using the master-slavemodel. Developed frameworks can be easily incorporated into any genetic algorithm,giving a universal mechanism for distribution of individuals and collection of the eval-uation results. This concept provides parallelization of genetic algorithms on variousdistributed architectures, including multiprocessors and computing clusters. Performedtests have shown that proposed frameworks achieve signicant speedup, especially whenevaluating large-scale problems. In addition, a case study from the eld of hydrologyis presented.