Adaptive Distributed Genetic Algorithms and Its VLSI Design
This paper presents a dynamic adaptation scheme for
the frequency of inter-deme migration in distributed genetic algorithms
(GA), and its VLSI hardware design. Distributed GA,
or multi-deme-based GA, uses multiple populations which evolve
concurrently. The purpose of dynamic adaptation is to improve
convergence performance so as to obtain better solutions. Through
simulation experiments, we proved that our scheme achieves better
performance than fixed frequency migration schemes.
[1] N. Yoshida, T. Yasuoka, T. Moriki, and T. Shimokawa, "VLSI Hardware
Design for Genetic Algorithms and Its Parallel and Distributed Extensions",
Int. J. of Knowledge-Based Intelligent Engineering Systems,
Vol. 5, No. 1, 2001, pp. 14-21.
[2] L. Davis (ed.), Handbook of Genetic Algorithms, Van Nostrand Reinhold,
1991.
[3] H. Sato, I. Ono and S. Kobayashi, "A New Generation Alternation Model
of Genetic Algorithms and Its Assessment" (in Japanese), J. Japanese
Society for Artificial Intelligence, Vol. 12, No. 5, 1997, pp. 734-744.
[4] M. Serra, T. Slater, J. C. Muzi and D. M. Miller, "The Analysis of One-
Dimensional Linear Cellular Automata and Their Aliasing Properties",
IEEE Trans. on Computer-Aided Design of Integrated Circuits and
Systems, Vol. 9, No. 7, 1990, pp. 767-788.
[5] M. Munetomo, Y. Takai, and Y. Sato, "An Efficient Migration Scheme
for Subpopulation-based Asynchronously Parallel Genetic Algorithms",
Proc. 5th Int. Conf. on GA, 1993, p. 649.
[6] M. A. Potter and K. A. De Jong, "A Cooperative Coevolutionary
Approach to Function Optimization", Proc. Third Conf. on Parallel
Problem Solving From Nature, 1994, pp. 249-257.
[7] Y. Nakamura, K. Oguri, et al., "High-Level Synthesis Design at NTT
Systems Labs", IEICE Trans. on Inf. & Syst., Vol. E76-D, No.9, 1993,
pp. 1047-1054.
[8] M. Mitchell, and S. Forrest, "Fitness Landscapes: Royal Road Functions",
Handbook of Evolutionary Computation (T. Back, D. Fogel, and
Z. Michalewicz, eds.), Oxford, 1997.
[1] N. Yoshida, T. Yasuoka, T. Moriki, and T. Shimokawa, "VLSI Hardware
Design for Genetic Algorithms and Its Parallel and Distributed Extensions",
Int. J. of Knowledge-Based Intelligent Engineering Systems,
Vol. 5, No. 1, 2001, pp. 14-21.
[2] L. Davis (ed.), Handbook of Genetic Algorithms, Van Nostrand Reinhold,
1991.
[3] H. Sato, I. Ono and S. Kobayashi, "A New Generation Alternation Model
of Genetic Algorithms and Its Assessment" (in Japanese), J. Japanese
Society for Artificial Intelligence, Vol. 12, No. 5, 1997, pp. 734-744.
[4] M. Serra, T. Slater, J. C. Muzi and D. M. Miller, "The Analysis of One-
Dimensional Linear Cellular Automata and Their Aliasing Properties",
IEEE Trans. on Computer-Aided Design of Integrated Circuits and
Systems, Vol. 9, No. 7, 1990, pp. 767-788.
[5] M. Munetomo, Y. Takai, and Y. Sato, "An Efficient Migration Scheme
for Subpopulation-based Asynchronously Parallel Genetic Algorithms",
Proc. 5th Int. Conf. on GA, 1993, p. 649.
[6] M. A. Potter and K. A. De Jong, "A Cooperative Coevolutionary
Approach to Function Optimization", Proc. Third Conf. on Parallel
Problem Solving From Nature, 1994, pp. 249-257.
[7] Y. Nakamura, K. Oguri, et al., "High-Level Synthesis Design at NTT
Systems Labs", IEICE Trans. on Inf. & Syst., Vol. E76-D, No.9, 1993,
pp. 1047-1054.
[8] M. Mitchell, and S. Forrest, "Fitness Landscapes: Royal Road Functions",
Handbook of Evolutionary Computation (T. Back, D. Fogel, and
Z. Michalewicz, eds.), Oxford, 1997.
@article{"International Journal of Information, Control and Computer Sciences:49781", author = "Kazutaka Kobayashi and Norihiko Yoshida and Shuji Narazaki", title = "Adaptive Distributed Genetic Algorithms and Its VLSI Design", abstract = "This paper presents a dynamic adaptation scheme for
the frequency of inter-deme migration in distributed genetic algorithms
(GA), and its VLSI hardware design. Distributed GA,
or multi-deme-based GA, uses multiple populations which evolve
concurrently. The purpose of dynamic adaptation is to improve
convergence performance so as to obtain better solutions. Through
simulation experiments, we proved that our scheme achieves better
performance than fixed frequency migration schemes.", keywords = "Genetic algorithms, dynamic adaptation, VLSI hardware.", volume = "3", number = "4", pages = "875-4", }