Sim-paramecium algorithm based on enhanced livability and competition

Authors:
Ming-Shen Jian;Ta Yuan Chou;Kun-Sian Sie;Long-Yeu Chung
Affiliations:
Department of Computer and Communication, Shu-Te University, Kaohsiung, Taiwan and Department of Applied Geoinformatics, Chia Nan University of Pharmacy & Science, Tainan, Taiwan;Department of Computer Science and Engineeing, National Sun Yat-sen University, Kaohsiung, Taiwan;Department of Computer Science and Engineeing, National Sun Yat-sen University, Kaohsiung, Taiwan;Department of Computer and Communication, Shu-Te University, Kaohsiung, Taiwan and Department of Applied Geoinformatics, Chia Nan University of Pharmacy & Science, Tainan, Taiwan
Venue:
ICS'09 Proceedings of the 13th WSEAS international conference on Systems
Year:
2009

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Abstract

This paper proposes a sim-paramesium genetic algorithm to enhance the searching and optimizing speed of classical genetic algorithms. Based upon classical genetic algorithms, the sim-paramesium genetic algorithm employs additional operators, such as asexual reproduction, competition, and livability in the survival operation. Taking the advantages of these three operators, the searching and optimizing speed can be increased. Experiments indicate that simulations with the proposed algorithm have a 47% improvement in convergence speed on the traveling salesman problem. Also, while applying the proposed method to solve the graph coloring problem, the proposed algorithm also has a 10% improvement in solution qualities. Furthermore, since these operators are additional parts to the original GA, the algorithm can be further improved by enhancing the operators, such as selection, crossover, and mutation.