Algorithmic information theory
Algorithmic information theory
Elements of an expert system for determining the satisfiability of general Boolean expressions
Elements of an expert system for determining the satisfiability of general Boolean expressions
Artificial Intelligence
Foundations of genetic programming
Foundations of genetic programming
Size Fair and Homologous Tree Crossovers for Tree Genetic Programming
Genetic Programming and Evolvable Machines
Neutrality and the Evolvability of Boolean Function Landscape
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming
Evolutionary Computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Algebraic simplification of GP programs during evolution
Proceedings of the 8th annual conference on Genetic and evolutionary computation
The halting probability in von neumann architectures
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Two fast tree-creation algorithms for genetic programming
IEEE Transactions on Evolutionary Computation
Behavioural Diversity and Filtering in GP Navigation Problems
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Program optimization by random tree sampling
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Semantic analysis of program initialisation in genetic programming
Genetic Programming and Evolvable Machines
Promoting phenotypic diversity in genetic programming
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Phenotypic diversity in initial genetic programming populations
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
An investigation of fitness sharing with semantic and syntactic distance metrics
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Examining the diversity property of semantic similarity based crossover
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
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Generating a random sampling of program trees with spec-ified function and terminal sets is the initial step of many program evolution systems. I present a theoretical and experimental analysis of the expected distribution of uniformly sampled programs, guided by algorithmic information theory. This analysis demonstrates that increasing the sample size is often an inefficient means of increasing the overall diversity of program behaviors (outputs). A novel sampling scheme (semantic sampling) is proposed that exploits semantics to heuristically increase behavioral diversity. An important property of the scheme is that no calls of the problem-specific fitness function are required. Its effective-ness at increasing behavioral diversity is demonstrated empirically for Boolean formulae. Furthermore, it is found to lead to statistically significant improvements in performance for genetic programming on parity and multiplexer problems.