Reinforced Genetic Programming
Genetic Programming and Evolvable Machines
Proceedings of the Genetic and Evolutionary Computation Conference
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Genetic Programming with Local Hill-Climbing
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Lexicographic Parsimony Pressure
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Grammatical Evolution: Evolving Programs for an Arbitrary Language
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Numeric Mutation as an Improvement to Symbolic Regression in Genetic Programming
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Self Generating Metaheuristics in Bioinformatics: The Proteins Structure Comparison Case
Genetic Programming and Evolvable Machines
The Hierarchical Fair Competition (HFC) Framework for Sustainable Evolutionary Algorithms
Evolutionary Computation
Increasing Population Diversity Through Cultural Learning
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Effects of code growth and parsimony pressure on populations in genetic programming
Evolutionary Computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Symbolic regression using abstract expression grammars
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Evolution of cartesian genetic programs capable of learning
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Improving symbolic regression with interval arithmetic and linear scaling
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
A simple but theoretically-motivated method to control bloat in genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
An analysis of diversity of constants of genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Real-time, non-intrusive evaluation of VoIP
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
A Methodology for Deriving VoIP Equipment Impairment Factors for a Mixed NB/WB Context
IEEE Transactions on Multimedia
Variance based selection to improve test set performance in genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Overfitting detection and adaptive covariant parsimony pressure for symbolic regression
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Validation sets for evolutionary curtailment with improved generalisation
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
Evolving estimators of the pointwise Hölder exponent with Genetic Programming
Information Sciences: an International Journal
Bootstrapping to reduce bloat and improve generalisation in genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
A bootstrapping approach to reduce over-fitting in genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Hi-index | 0.00 |
Typically, an individual in Genetic Programming (GP) can not make the most of its genetic inheritance. Once it is mapped, its fitness is immediately evaluated and it survives only until the genetic operators and its competitors eliminate it. Thus, the key to survival is to be born strong. This paper proposes a simple alternative to this powerlessness by allowing an individual to tune its internal nodes and going through several evaluations before it has to compete with other individuals. We demonstrate that this system, Chameleon, outperforms standard GP over a selection of symbolic regression type problems on both training and test sets; that the system works harmoniously with two other well known extensions to GP, that is, linear scaling and a diversity promoting tournament selection method; that it can benefit dramatically from a simple cache; that adding to functions set does not always add to the tuning expense; and that tuning alone can be enough to promote smaller trees in the population. Finally, we touch upon the consequences of ignoring the effects of complexity when focusing on just the tree sizes to induce parsimony pressure in GP populations.