Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Learning and evolution in neural networks
Adaptive Behavior
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Artificial Life
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in Genetic Programming
Genetic Programming and Evolvable Machines
An Empirical Study of Multipopulation Genetic Programming
Genetic Programming and Evolvable Machines
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Is The Perfect The Enemy Of The Good?
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
An analysis of cooperative coevolutionary algorithms
An analysis of cooperative coevolutionary algorithms
Improving symbolic regression with interval arithmetic and linear scaling
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
A multistage approach to cooperatively coevolving feature construction and object detection
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Towards Dynamic Fitness Based Partitioning for IntraVascular UltraSound Image Analysis
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
An evolutionary framework for colorimetric characterization of scanners
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Optimizing computed tomographic angiography image segmentation using fitness based partitioning
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Computing surrogate constraints for multidimensional Knapsack problems using evolution strategies
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
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Cooperative coevolution has proven to be a promising technique for solving complex combinatorial optimization problems. In this paper, we present four different strategies which involve cooperative coevolution of a genetic program and of a population of constants evolved by a genetic algorithm. The genetic program evolves expressions that solve a problem, while the genetic algorithm provides "good" values for the numeric terminal symbols used by those expressions. Experiments have been performed on three symbolic regression problems and on a "real-world" biomedical application. Results are encouraging and confirm that our coevolutionary algorithms can be used effectively in different domains.