Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Linear Genetic Programming (Genetic and Evolutionary Computation)
Linear Genetic Programming (Genetic and Evolutionary Computation)
Genetic Programming Crossover: Does It Cross over?
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Semantic Aware Crossover for Genetic Programming: The Case for Real-Valued Function Regression
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Approximating geometric crossover in semantic space
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Semantically embedded genetic programming: automated design of abstract program representations
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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We consider a class of adaptive, globally-operating, semantic-based embeddings of programs into discrete multidimensional spaces termed prespaces. In the proposed formulation, the original space of programs and its prespace are bound with a learnable mapping, where the process of learning is aimed at improving the overall locality of the new representation with respect to program semantics. To learn the mapping, which is formally a permutation of program locations in the prespace, we propose two algorithms: simple greedy heuristics and an evolutionary algorithm. To guide the learning process, we use a new definition of semantic locality. In an experimental illustration concerning four symbolic regression domains, we demonstrate that an evolutionary algorithm is able to improve the embedding designed by means of greedy search, and that the learned prespaces usually offer better search performance than the original program space.