Kolmogorov complexity and its applications
Handbook of theoretical computer science (vol. A)
Genetic programming using a minimum description length principle
Advances in genetic programming
Multi-Objective Methods for Tree Size Control
Genetic Programming and Evolvable Machines
Genetic diversity as an objective in multi-objective evolutionary algorithms
Evolutionary Computation
Balancing accuracy and parsimony in genetic programming
Evolutionary Computation
Evolutionary induction of sparse neural trees
Evolutionary Computation
Sustaining diversity using behavioral information distance
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A quantitative study of learning and generalization in genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Generating corpora of activities of daily living and towards measuring the corpora's complexity
CAVE'12 Proceedings of the First international conference on Cognitive Agents for Virtual Environments
Controllable procedural map generation via multiobjective evolution
Genetic Programming and Evolvable Machines
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Model complexity is key concern to any artificial learning system due its critical impact on generalization. However, EC research has only focused phenotype structural complexity for static problems. For sequential decision tasks, phenotypes that are very similar in structure, can produce radically different behaviors, and the trade-off between fitness and complexity in this context is not clear. In this paper, behavioral complexity is measured explicitly using compression, and used as a separate objective to be optimized (not as an additional regularization term in a scalar fitness), in order to study this trade-off directly.