Improving GP classifier generalization using a cluster separation metric
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Diverse committees vote for dependable profits
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolving robust GP solutions for hedge fund stock selection in emerging markets
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A New Schema Survival and Construction Theory for One-Point Crossover
Computational Intelligence and Security
Learning General Solutions through Multiple Evaluations during Development
ICES '08 Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware
Methods for evolving robust programs
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
PAC learning and genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A self-scaling instruction generator using cartesian genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Validation sets for evolutionary curtailment with improved generalisation
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
Emergent generality of adapted locomotion gaits of simulated snake-like robot
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Genetic programming, validation sets, and parsimony pressure
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Novelty-Based fitness: an evaluation under the santa fe trail
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Evolving priority scheduling heuristics with genetic programming
Applied Soft Computing
Comparing the robustness of grammatical genetic programming solutions for femtocell algorithms
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Environmental Modelling & Software
Where should we stop? an investigation on early stopping for GP learning
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
A bootstrapping approach to reduce over-fitting in genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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In genetic programming (GP), learning problems can be classified broadly into two types: those using data sets, as in supervised learning, and those using an environment as a source of feedback. An increasing amount of research has concentrated on the robustness or generalization ability of the programs evolved using GP. While some of the researchers report on the brittleness of the solutions evolved, others proposed methods of promoting robustness/generalization. It is important that these methods are not ad hoc and are applicable to other experimental setups. In this paper, learning concepts from traditional machine learning and a brief review of research on generalization in GP are presented. The paper also identifies problems with brittleness of solutions produced by GP and suggests a method for promoting robustness/generalization of the solutions in simulating learning behaviors using GP