Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Local search characteristics of incomplete SAT procedures
Artificial Intelligence
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
The Adaptive Constraint Engine
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Automated discovery of composite SAT variable-selection heuristics
Eighteenth national conference on Artificial intelligence
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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Methods of adaptive constraint satisfaction have recently become of interest to overcome the limitations imposed on “black-box” search algorithms by the no free lunch theorems. Two methods that each use an evolutionary algorithm to adapt to particular classes of problem are the CLASS system of Fukunaga and the evolutionary constraint algorithm work of Bain et al. We directly compare these methods, demonstrating that although special purpose methods can learn excellent algorithms, on average standard evolutionary operators perform even better, and are less susceptible to the problems of bloat and redundancy.