Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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
Nested Partitions Method for Global Optimization
Operations Research
Ant colony optimization theory: a survey
Theoretical Computer Science
The interacting-particle algorithm with dynamic heating and cooling
Journal of Global Optimization
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
Sequential Monte Carlo simulated annealing
Journal of Global Optimization
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We propose a stochastic search algorithm for solving non-differentiable optimization problems. At each iteration, the algorithm searches the solution space by generating a population of candidate solutions from a parameterized sampling distribution. The basic idea is to convert the original optimization problem into a differentiable problem in terms of the parameters of the sampling distribution, and then use a quasi-Newton-like method on the reformulated problem to find improved sampling distributions. The algorithm combines the strength of stochastic search from considering a population of candidate solutions to explore the solution space with the rapid convergence behavior of gradient methods by exploiting local differentiable structures. We provide numerical examples to illustrate its performance.