Cooling schedules for optimal annealing
Mathematics of Operations Research
Uniform crossover in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
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
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Journal of Global Optimization
A measure-theoretic analysis of stochastic optimization
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
Neuroannealing: martingale optimization for neural networks
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Evolutionary annealing: global optimization in measure spaces
Journal of Global Optimization
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Standard genetic algorithms can discover good fitness regions and later forget them due to their Markovian structure, resulting in suboptimal performance. Real-Space Evolutionary Annealing (REA) hybridizes simulated annealing and genetic algorithms into a provably convergent evolutionary algorithm for Euclidean space that relies on non-Markovian selection. REA selects any previously observed solution from an approximated Boltzmann distribution using a cooling schedule. This method enables REA to escape local optima while retaining information about prior generations. In parallel work, REA has been generalized to arbitrary measure spaces and shown to be asymptotically convergent to the global optima. This paper compares REA experimentally to six popular optimization algorithms, including Differential Evolution, Particle Swarm Optimization, Correlated Matrix Adaptation Evolution Strategies, the real-coded Bayesian Optimization Algorithm, a real-coded genetic algorithm, and simulated annealing. REA converges faster to the global optimum and succeeds more often on two out of three multimodal, non-separable benchmarks and performs strongly on all three. In particular, REA vastly outperforms the real-coded genetic algorithm and simulated annealing, proving that the hybridization is better than either algorithm alone. REA is therefore an interesting and effective algorithm for global optimization of difficult fitness functions.