Abstract
Generally, as for Genetic Algorithms (GAs), it is not always optimal search efficiency, because genetic parameters (crossover rate, mutation rate and so on) are fixed. For this problem, we have already proposed Fuzzy Adaptive Search Method for GA (FASGA) that is able to tune the genetic parameters according to the search stage by the fuzzy reasoning. On the other hand, in order to improve the solution quality of GA, Parallel Genetic Algorithm (PGA) based on the local evolution in plural sub-populations (islands) and the migration of individuals between islands has been researched.
In this research, Fuzzy Adaptive Search method for Parallel GA (FASPGA) combined FASGA with PGA is proposed. Moreover as the improvement method for FASPGA, Diversity Measure based Fuzzy Adaptive Search method for Parallel GA (DM-FASPGA) is also proposed. Computer simulation was carried out to confirm the efficiency of the proposed method and the simulation results are also reported in this paper.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Subbu, R., Bonissone, P.: A Retrospective View of Fuzzy Control of Evolutionary Algorithm Resources. In: Proc. FUZZ-IEEE 2003, pp. 143–148 (2003)
Holland, J.H.: Adaptation in Netural and Artifical System. University of Michigan Press, Ann Arbor (1992)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Xu, H.Y., Vukovich, G.: A Fuzzy Genetic Algorithm with Effective Search and Optimization. In: Int’l J. Conf. on Neural Networks (IJCNN’93), pp. 2967–2970 (1993)
Lee, M.A., Takagi, H.: Dynamic Control of Genetic Algorithms using Fuzzy Logic Techniques. In: Proc. of 5th International Conference on Genetic Algorithms (ICGA’93), pp. 76–83 (1993)
Herrera, F., Lozamo, M.: Adaptive Genetic Algorithms Based on Fuzzy Tecniques. In: Proc. Sixth Int’l Conf. on Information Processing and Management of Uncertainty in Knowledge Based System (IPMU’96), pp. 775–780 (1996)
Maeda, Y.: A Method for Improving Search performance of GA with Fuzzy Rules (In Japanese). In: Proc. of the 6th Intelligent System symposium, vol. 3, pp. 27–30 (1996)
Maeda, Y.: Fuzzy Adaptive Search Method for Genetic Programming. International Journal of Advanced Computational Intelligence 3(2), 131–135 (1999)
Nang, J., Matsuo, K.: A Survey on the Parallel Genetic Algorithms. Journal of the Society of Instrument and Control Engineering 33(6), 500–509 (1994)
Starkweather, T., Whitley, D., Mathisa, K.: Optimization Using Distributed Genetic Algorithms. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, Springer, Heidelberg (1991)
Tanese, R.: Distributed Genetic Algorithms. In: Proc. 3rd International Conf on Genetic Algorithms, pp. 434–439. Morgan Kaufmann, San Francisco (1989)
Cant’u-Paz, E.: A Survey on the Parallel Genetic Algorithms. Calculateurs Paralleles (1998)
Mühlenbein, H.: Evolution in Time and Space: The Parallel Genetic Algorithm. In: Rawlins, G. (ed.) FOGA-1, pp. 316–337. Morgan Kaufmann, San Francisco (1991)
Hiroyasu, T., Miki, M., Negami, M.: Distributed Genetic Algorithms with Randomized Migration Rate. In: IEEE Proceedings of Systems, Man and Cybernetics Conference (SMC’99), vol. 1, pp. 689–694 (1999)
Miki, M., Hiroyasu, T., Kaneco, O., Hatanaka, K.: A Parallel Genetic Algorithm with Distributed Environment Scheme. In: IEEE Proceedings of Systems, Man and Cybernetics Conference (SMC’99), pp. 695–700 (1999)
Li, Q., Maeda, Y.: Adaptive Search Method for Parallel Genetic Algorithms Used Fuzzy Reasoning. In: The 23rd Annual Conference of the Robotics Society of Japan, 2B15 (2004)
Li, Q., Maeda, Y.: Parallel Genetic Algorithms with Adaptive Migration Rate Tuned by Fuzzy Reasoning. In: Proceedings of the Fourth International Symposium on Human and Artificial Intelligence Systems (HART 2004), pp. 259–264 (2004)
Maeda, Y., Li, Q.: Parallel Genetic Algorithm with Adaptive Genetic Parameters Tuned by Fuzzy Reasoning. International Journal of Innovating Computing, Information and Control 1(1), 95–107 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Maeda, Y., Li, Q. (2007). Fuzzy Adaptive Search Method for Parallel Genetic Algorithm Tuned by Evolution Degree Based on Diversity Measure. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds) Foundations of Fuzzy Logic and Soft Computing. IFSA 2007. Lecture Notes in Computer Science(), vol 4529. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72950-1_67
Download citation
DOI: https://doi.org/10.1007/978-3-540-72950-1_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72917-4
Online ISBN: 978-3-540-72950-1
eBook Packages: Computer ScienceComputer Science (R0)