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
Foundations of genetic programming
Foundations of genetic programming
Genetic Programming and Evolvable Machines
H-PIPE: Facilitating Hierarchical Program Evolution through Skip Nodes
H-PIPE: Facilitating Hierarchical Program Evolution through Skip Nodes
Learning computer programs with the bayesian optimization algorithm
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Linkage Problem, Distribution Estimation, and Bayesian Networks
Evolutionary Computation
A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming
Evolutionary Computation
Probabilistic incremental program evolution
Evolutionary Computation
Bayesian automatic programming
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Evolving computer programs without subtree crossover
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Probabilistic developmental program evolution
Proceedings of the 2010 ACM Symposium on Applied Computing
Structural difficulty in estimation of distribution genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Integrating feature selection into program learning
AGI'13 Proceedings of the 6th international conference on Artificial General Intelligence
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I present a new estimation-of-distribution approach to program evolution where distributions are not estimated over the entire space of programs. Rather, a novel representation-building procedure that exploits domain knowledge is used to dynamically select program subspaces for estimation over. This leads to a system of demes consisting of alternative rep-resentations (i.e. program subspaces) that are maintained simultaneously and managed by the overall system. Meta-optimizing semantic evolutionary search (MOSES), a program evolution system based on this approach, is described, and its representation-building subcomponent is analyzed in depth. Experimental results are also provided for the overall MOSES procedure that demonstrate good scalability.