Classifier systems and genetic algorithms
Artificial Intelligence
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Genetic Reinforcement Learning for Neurocontrol Problems
Machine Learning - Special issue on genetic algorithms
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Artificial Life
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Properties of the Bucket Brigade
Proceedings of the 1st International Conference on Genetic Algorithms
A Coevolutionary Approach to Learning Sequential Decision Rules
Proceedings of the 6th International Conference on Genetic Algorithms
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic reinforcement learning through symbiotic evolution forfuzzy controller design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy CoCo: a cooperative-coevolutionary approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This paper proposes a Symbiotic Genetic Algorithm with Local-and-Global mapping search (SGA-LG) for fuzzy controller design under reinforcement learning environments. The objective of the proposed SGA-LG is to increase the reinforcement fuzzy controller design efficacy and efficiency. SGA-LG operates in two concurrently evolving searches: the local mapping search and the global mapping search. The local-mapping search helps to find the well-performed local rules. A population is created in this search, and each individual in the population encodes only one fuzzy rule. An elite strategy is adopted, where the top-half best-performing individuals, the elites, are reproduced directly to the next generation, and parents are selected from the elites only. For global-mapping search, another population is created, where each individual encodes a whole fuzzy network as opposed to a single rule. The objective is to determine which local rules designed in the local-mapping search should be combined together to achieve a good fuzzy network. To demonstrate the performance of SGA-LG, it is applied to cart-pole and ball-and-beam system controls. The efficacy and efficiency of SGA-LG are verified by comparing with other GAs, evolution strategy and evolutionary programming based fuzzy controller designs.