Pure adaptive search in global optimization
Mathematical Programming: Series A and B
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
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Discrete optimization via approximate annealing adaptive search with stochastic averaging
Proceedings of the Winter Simulation Conference
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The Annealing Adaptive Search (AAS) algorithm searches the feasible region of an optimization problem by generating candidate solutions from a sequence of Boltzmann distributions. However, the difficulty of sampling from a Boltzmann distribution at each iteration of the algorithm limits its applications to practical problems. To address this difficulty, we propose an approximation of AAS, called Model-based Annealing Random Search (MARS), that samples solutions from a sequence of surrogate distributions that iteratively approximate the target Boltzmann distributions. We present the global convergence properties of MARS by exploiting its connection to the stochastic approximation method and report on numerical results.