Statistical machine learning and combinatorial optimization
Theoretical aspects of evolutionary computing
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Linkage Problem, Distribution Estimation, and Bayesian Networks
Evolutionary Computation
The equation for response to selection and its use for prediction
Evolutionary Computation
Optimization in continuous domain by real-coded estimation of distribution algorithm
Design and application of hybrid intelligent systems
Extraction of informative genes from microarray data
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Probabilistic modeling for continuous EDA with Boltzmann selection and Kullback-Leibeler divergence
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Optimizing a new nonlinear reinforcement scheme with Breeder genetic algorithm
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
A nonlinear reinforcement scheme for stochastic learning automata
MMACTEE'06 Proceedings of the 8th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering
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This paper proposes an algorithm for combinatorial optimizations that uses reinforcement learning and estimation of joint probability distribution of promising solutions to generate a new population of solutions. We call it Reinforcement Learning Estimation of Distribution Algorithm (RELEDA). For the estimation of the joint probability distribution we consider each variable as univariate. Then we update the probability of each variable by applying reinforcement learning method. Though we consider variables independent of one another, the proposed method can solve problems of highly correlated variables. To compare the efficiency of our proposed algorithm with other Estimation of Distribution Algorithms (EDAs) we provide the experimental results of the two problems: four peaks problem and bipolar function.