Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Dynamic Training Subset Selection for Supervised Learning in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Balancing accuracy and parsimony in genetic programming
Evolutionary Computation
Evolving teamwork and coordination with genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Code growth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Parallel linear genetic programming for multi-class classification
Genetic Programming and Evolvable Machines
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Genetic programming evolves Lisp-like programs rather than fixed size linear strings. This representational power combined with generality makes genetic programming an interesting tool for automatic programming and machine learning. One weakness is the enormous time required for evolving complex programs. In this paper we present a method for accelerating evolution speed of genetic programming by active selection of fitness cases during the run. In contrast to conventional genetic programming in which all the given training data are used repeatedly, the presented method evolves programs using only a subset of given data chosen incrementally at each generation. This method is applied to the evolution of collective behaviors for multiple robotic agents. Experimental evidence supports that evolving programs on an incrementally selected subset of fitness cases can significantly reduce the fitness evaluation time without sacrificing generalization accuracy of the evolved programs.