A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Cooling schedules for optimal annealing
Mathematics of Operations Research
Convergence of the simulated annealing algorithm for continuous global optimization
Journal of Optimization Theory and Applications
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Schemata, Distributions and Graphical Models in Evolutionary Optimization
Journal of Heuristics
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Journal of Global Optimization
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Paper: The parallel genetic algorithm as function optimizer
Parallel Computing
Quad search and hybrid genetic algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Real-space evolutionary annealing
Proceedings of the 13th annual conference on Genetic and evolutionary computation
An Inflationary Differential Evolution Algorithm for Space Trajectory Optimization
IEEE Transactions on Evolutionary Computation
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Stochastic optimization methods such as evolutionary algorithms and Markov Chain Monte Carlo methods usually involve a Markov search of the optimization domain. Evolutionary annealing is an evolutionary algorithm that leverages all the information gathered by previous queries to the cost function. Evolutionary annealing can be viewed either as simulated annealing with improved sampling or as a non-Markovian selection mechanism for evolutionary algorithms. This article develops the basic algorithm and presents implementation details. Evolutionary annealing is a martingale-driven optimizer, where evaluation yields a source of increasingly refined information about the fitness function. A set of experiments with twelve standard global optimization benchmarks is performed to compare evolutionary annealing with six other stochastic optimization methods. Evolutionary annealing outperforms other methods on asymmetric, multimodal, non-separable benchmarks and exhibits strong performance on others. It is therefore a promising new approach to global optimization.