A course in fuzzy systems and control
A course in fuzzy systems and control
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Evolutionary programming-based univector field navigation methodfor past mobile robots
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary design of fuzzy rule base for nonlinear system modeling and control
IEEE Transactions on Fuzzy Systems
A case-based approach for coordinated action selection in robot soccer
Artificial Intelligence
Multiobjective quantum-inspired evolutionary algorithm for fuzzy path planning of mobile robot
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Analysis of strategy in robot soccer game
Neurocomputing
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A rule selection scheme of evolutionary algorithm is proposed to design fuzzy path planner for shooting ability in robot soccer. The fuzzy logic is good for the system that works with ambiguous information. Evolutionary algorithm is employed to deal with difficulty and tediousness in deriving fuzzy control rules. Generic evolutionary algorithm, however, evaluate and select chromosomes which may include inferior genes, and generate solutions with uncertainty. To ameliorate this problem, we propose a recombinant rule selection method for gene level selection, which grades genes at the same position in the chromosomes and recombine new parent for next generation. The method was evaluated with application of designing the fuzzy path planner, where each fuzzy rule was encoded as a gene. Simulation and experimental results showed the effectiveness and the applicability of the proposed method.