Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Hybrid simplex genetic algorithm for blind equalization using RBF networks
Mathematics and Computers in Simulation
Stable adaptive fuzzy controllers with application to inverted pendulum tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A novel stochastic optimization algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Fuzzy granulation-based cascade fuzzy neural networks optimized by GA-RSL
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
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In this paper, we propose three effective hybrid random signal-based learning (RSL) algorithms which are a combination of RSL with simulated annealing (SA) and a genetic algorithm (GA) to obtain a global solution that can be used in combinatorial optimization problems. GAs are becoming more popular because of their relative simplicity and robustness. GAs are global search techniques for non-linear optimization, but they are not good at fine-tuning solutions. RSL is similar to the reinforcement learning of neural networks using random signals. It can find an accurate solution in local search space. However, it is poor at hill-climbing, whereas simulated annealing has the ability to perform probabilistic hill-climbing. Therefore, combining them yields effective hybrid algorithms, i.e. hybrid RSL algorithms, with the merits of both. To check the generalization ability of the proposed algorithms, the optimizations of several benchmark test functions are considered, while the optimization of a fuzzy logic controller for the inverted pendulum is detailed to show the applicability of the proposed algorithms to fuzzy control.