An Improved Parallel Adaptive Genetic Algorithm Based on Pareto Front for Multi-objective Problems

Authors:
Guangyuan Liu;Jingjun Zhang;Ruizhen Gao;Yanmin Shang
Affiliations:
-;-;-;-
Venue:
KAM '09 Proceedings of the 2009 Second International Symposium on Knowledge Acquisition and Modeling - Volume 02
Year:
2009

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A PTS-PGATS based approach for data-intensive scheduling in data grids

Frontiers of Computer Science in China

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Abstract

For multi-objective optimization problems, we introduced IPAGA (Improved Parallel Adaptive Genetic Algorithm) in this paper, a new parallel genetic algorithm which is based on Pareto Front. In this Algorithm, the non-dominated-set is constructed by the method of exclusion. The evolution population adopts the adaptive-crossover and adaptive-mutation probability, which can adjust the search scope according to solution quality. The results show that the parallel genetic algorithm developed in this paper is efficient.