Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
A Survey And Analysis Of Diversity Measures In Genetic Programming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
CGP visits the Santa Fe trail: effects of heuristics on GP
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Schema theory for genetic programming with one-point crossover and point mutation
Evolutionary Computation
Visualizing tree structures in genetic programming
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Grammar-based Genetic Programming: a survey
Genetic Programming and Evolvable Machines
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
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Genetic Programming explores the problem search space by means of operators and selection. Mutation and crossover operators apply uniformly, while selection is the driving force for the search. Constrained GP changes the uniform exploration to pruned non-uniform, skipping some subspaces and giving preferences to others, according to some heuristics. Adaptable Constrained GP is a methodology for discovery of such useful heuristics. Both methodologies have previously demonstrated their surprising capabilities using only first-order (parent-child) heuristics. Recently, they have been extended to second-order (parent-children) heuristics. This paper describes the second-order processing, and illustrates the usefulness and efficiency of this approach using a simple problem specifically constructed to exhibit strong second-order structure.