Performance Analysis of Genetic Algorithm for Function Optimization in Multicore Platform Using DEAP

Abstract

Meta-heuristic algorithms are applied to find good or near-optimal solutions at a reasonable computational cost and time by exploring the search space in an efficient way. Parallelization and distributed computing techniques are solutions to enhance the algorithmic performance. The objective of this work is to analyze the performance of genetic algorithm for function optimization in multicore platform using distributed evolutionary algorithms in Python (DEAP) framework. The analysis is done based on optimal value obtained and the execution time taken to run benchmark functions. Ten benchmark functions of fixed dimensions and five benchmark functions with variable dimensions are considered during experimentation. From the results, we infer that the performance of GA in multicore platform is efficient in terms of computing speed and finding optimal value for the selected standard benchmark problems.