Abstract
In this paper, we propose a Parallel Genetic Algorithm (PGA) based on a modified survival method and discuss its efficient implementation. For parallel computation, we use a hybrid distributed architecture based on the coarse-grain and fine-grain. Moreover, we propose a modified survival-based GA using tournament selection method. To show the validity of a proposed PGA, we evaluate its performance with optimization problems such as DeJong’s functions, mathematical function, and set covering problem. In addition, we implement a PGA processor with ALTERA EP2A40672F FPGA device. The experimental results will be shown that proposed PGA remarkably improves the speed of finding optimal solution than single GAP.
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Scott, S.D., Samal, A., Seth, S.: HGA: A hardware-based genetic algorithm. In: Proceedings of ACM/SIMDA 3rd International Symposium on FPGA, pp. 53–59 (1995)
Cantu-Paz, E.: A Survey of Parallel Genetic Algorithms. IiiiGAL R. 97003 (1997)
Salami, M.: Multiple genetic algorithm processor for hardware optimization. In: Higuchi, T., Iwata, M., Weixin, L. (eds.) ICES 1996. LNCS, vol. 1259, pp. 249–259. Springer, Heidelberg (1997)
Turton, B.C.H., Arslan, T., Horrocks, D.H.: A Hardware Architecture for a Parallel Genetic Algorithm for Image Registration. In: Proceedings of IEE Colloquium on Genetic Algorithms, pp. 11/1–11/6 (1994)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning (1989)
Pelikan, M., Parthasarathy, P., Ramraj, A.: Fine-grained Parallel Genetic Algorithms in Charm++. ACM Crossroad Magazine: Parallel Computing (2002)
Pereira, C., Lapa, C.: Coarse-grained parallel genetic algorithm applied to a nuclear reactor core design optimization problem. Annals of Nuclear Energy, 555–565 (2003)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996)
Berntsson, J., Tang, M.: A convergence model for asynchronous parallel genetic algorithms. The 2003 Congress on Evolutionary Computation 4, 2627–2634 (2003)
Gagne, C., Parizeau, M., Dubreuil, M.: The Master-Slave Archtecture for Evolutionary Computations Revisited. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1578–1579 (2003)
Yoshida, N., Yasuaka, T., Moriki, T.: Parallel and Distributed Processing in VLSI Implementation of Genetic Algorithms. In: Proceedings of the Third International ICSC Symposia on Intelligent Industrial Autimation, On Soft Computnig, pp. 450–454 (1999)
Konfrst, Z.: Parallel Genetic Algorithms: Adcances, Computing Trends, Applications and Perspectives. In: Proceedings of the 18th International Parallel and Distributed Processing Symposium, pp. 26–34 (2004)
Sandip, S.: Minimal cost Set Covering using probabilistic methods. In: Proceedings of ACM/SIGAPP Symposium on Applied Computing, pp. 157–164 (1993)
Turton, B.C.H., Arslan, T.: A Parallel Gentic VLSI Architecture for a Combinatorial Real-Time Application-Disc Scheduling. In: First International conference on Genetic Algorithms in Engineering Systems, pp. 493–498 (1995)
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Kim, DS., Kim, HS., Lee, YS., Chung, DJ. (2005). On the Design of a Parallel Genetic Algorithm Based on a Modified Survival Method for Evolvable Hardware. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_67
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DOI: https://doi.org/10.1007/11494669_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
Online ISBN: 978-3-540-32106-4
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