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Real-Space Evolutionary Annealing (2011)
Alan J Lockett
and
Risto Miikkulainen
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.
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Citation:
In
Proceedings of the 2011 Genetic and Evolutionary Computation Conference (GECCO-2011)
2011.
Bibtex:
@inproceedings{lockett:gecco2011, title={Real-Space Evolutionary Annealing}, author={Alan J Lockett and Risto Miikkulainen}, booktitle={Proceedings of the 2011 Genetic and Evolutionary Computation Conference (GECCO-2011)}, url="http://www.cs.utexas.edu/users/ai-lab/?lockett:gecco11", year={2011} }
People
Alan J. Lockett
Ph.D. Alumni
alan lockett [at] gmail com
Risto Miikkulainen
Faculty
risto [at] cs utexas edu
Projects
Learning Strategic Behavior in Sequential Decision Tasks
2009 - 2014
Areas of Interest
Evolutionary Computation
Theory of Evolutionary Computation
Software/Data
PyEC
Python package containing source code for Evolutionary Annealing along with a number of other evolutionary and stochasti...
2011
Labs
Neural Networks