Evolutionary identification of macro-mechanical models
Advances in genetic programming
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
Convergence Rates For The Distribution Of Program Outputs
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
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
Functional modularity for genetic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Towards Understanding the Effects of Locality in GP
MICAI '09 Proceedings of the 2009 Eighth Mexican International Conference on Artificial Intelligence
Semantic building blocks in genetic programming
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Learnable embeddings of program spaces
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Medial crossovers for genetic programming
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
An ecological approach to measuring locality in linear genotype to phenotype maps
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Operator equalisation for bloat free genetic programming and a survey of bloat control methods
Genetic Programming and Evolvable Machines
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We propose an alternative program representation that relies on automatic semantic-based embedding of programs into discrete multidimensional spaces. An embedding imposes a well-structured hypercube topology on the search space, endows it with a semantic-aware neighborhood, and enables convenient search using Cartesian coordinates. The embedding algorithm consists in locality-driven optimization and operates in abstraction from a specific fitness function, improving locality of all possible fitness landscapes simultaneously. We experimentally validate the approach on a large sample of symbolic regression tasks and show that it provides better search performance than the original program space. We demonstrate also that semantic embedding of small programs can be exploited in a compositional manner to effectively search the space of compound programs.