Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Nested Partitions Method for Global Optimization
Operations Research
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
A Model Reference Adaptive Search Method for Global Optimization
Operations Research
On the performance of the cross-entropy method
Winter Simulation Conference
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Proceedings of the Winter Simulation Conference
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We propose a random search algorithm for black-box optimization with discrete decision variables. The algorithm is based on the recently introduced Model-based Annealing Random Search (MARS) for global optimization, which samples candidate solutions from a sequence of iteratively focusing distribution functions over the solution space. In contrast with MARS, which requires a sample size (number of candidate solutions) that grows at least polynomially with the number of iterations for convergence, our approach employs a stochastic averaging idea and uses only a small constant number of candidate solutions per iteration. We establish global convergence of the proposed algorithm and provide numerical examples to illustrate its performance.