Abstract
Building block hypothesis suggests that the highly-fit low-order schemata recombine with each other to form even more highly-fit high-order ones. One may naturally surmise that the coding should be designed to supply adequate superior order-1 schemata. In this paper, it is showed that, if superior order-1 building blocks are provided at most of the loci, there is likely to be remarkable fitness differences among high-order schemata, which indicates the existence of ‘pulse-shaped’ peaks on the curve of the fitness function. And fitness differences among the individuals are so great within the neighborhoods of these peaks that diversity loss tends to occur when searching within these regions. The results of this paper may to some degree explain why additional measures to maintain diversity should be taken to improve the local search performance of a simple genetic algorithm (GA).
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Recommended by Editorial Board member Sungshin Kim under the direction of Editor Young Il Lee. This work was supported by SRF for ROCS, SEM, China, and BK21, Korea.
Hongqiang Mo received the B.E. and Ph.D. degrees in Automatic Control Engineering from South China University of Technology in 1996, and 2001, respectively. His research interests include evolutionary computation, process control, and process signal processing.
Zhong Li received the B.Sc., M.Sc., and Ph.D. degrees from Sichuan University, Jinan University, and South China University of Technology, in 1989, 1996, and 2000, respectively. He obtained his Dr. of Science (Habilitation) from FernUniversität in Hagen in 2007. His research interest includes fuzzy logic and fuzzy control, chaos theory and chaos control, intelligent computation and control, complex networks, swarm intelligence.
Jin Bae Park received the B.E. degree in Electrical Engineering from Yonsei University, Seoul, Korea, and the M.S. and Ph.D. degrees in Electrical Engineering from Kansas State University, Manhattan, in 1977, 1985, and 1990, respectively. Since 1992, he has been with the Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, where he is currently a professor. His research interests include robust control and filtering, nonlinear control, mobile robot, fuzzy logic control, neural networks, genetic algorithms, and Hadamard transform spectroscopy. He serves as the Vice-President for the Institute of Control, Robot, and Systems Engineers (ICROS) (2009–2010) and Editor-in-Chief for the International Journal of Control, Automation, and Systems (IJCAS) (2006–2010).
Young Hoon Joo received the B.S., M.S., and Ph.D. degrees in Electrical Engineering from Yonsei University, Seoul, Korea, in 1982, 1984, and 1995, respectively. He worked with Samsung Electronics Company, Seoul, Korea, from 1986 to 1995, as a project manager. He was with the University of Houston, Houston, TX, from 1998 to 1999, as a visiting professor in the Department of Electrical and Computer Engineering. He is currently a professor in the School of Electronic and Information Engineering, Kunsan National University, Korea. His major interest is mainly in the field of intelligent robot, intelligent control, human-robot interaction(HRI), and nonlinear systems control. He is serving as President for Korea Institute of Intelligent Systems (KIIS) (2008–2009) and Editor for the Internatioal Journal of Control, Automation, and Systems (IJCAS) (2008–2010).
Xiangyang Li received the Ph.D. degree in Automatic Control Engineering from South China University of Technology in 2001. His research interests include machine learning, embedded systems and wireless sensor networks.
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Mo, H., Li, Z., Park, J.B. et al. Fitness landscape for simple genetic algorithms supplied with adequate superior order-1 building blocks. Int. J. Control Autom. Syst. 8, 135–140 (2010). https://doi.org/10.1007/s12555-010-0117-8
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DOI: https://doi.org/10.1007/s12555-010-0117-8