Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Multi-Objective Methods for Tree Size Control
Genetic Programming and Evolvable Machines
Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions
Evolutionary Computation
Comparison of tree and graph encodings as function of problem complexity
Proceedings of the 9th annual conference on Genetic and evolutionary computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Semantic building blocks in genetic programming
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Coevolution of Fitness Predictors
IEEE Transactions on Evolutionary Computation
A GPU-based implementation of an enhanced GEP algorithm
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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A key to the success of any genetic programming process is the use of a good alphabet of atomic building blocks from which solutions can be evolved efficiently. An alphabet that is too granular may generate an unnecessarily large search space; an inappropriately coarse grained alphabet may bias or prevent finding optimal solutions. Here we introduce a method that automatically identifies a small alphabet for a problem domain. We process solutions on the complexity-optimality Pareto front of a number of sample systems and identify terms that appear significantly more frequently than merited by their size. These terms are then used as basic building blocks to solve new problems in the same problem domain. We demonstrate this process on symbolic regression for a variety of physics problems. The method discovers key terms relating to concepts such as energy and momentum. A significant performance enhancement is demonstrated when these terms are then used as basic building blocks on new physics problems. We suggest that identifying a problem-specific alphabet is key to scaling evolutionary methods to higher complexity systems.