The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
A Representation for the Adaptive Generation of Simple Sequential Programs
Proceedings of the 1st International Conference on Genetic Algorithms
Improving the Performance of Genetic Algorithms in Classifier Systems
Proceedings of the 1st International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
General Layout of City Pedestrian Bridge
ICIII '08 Proceedings of the 2008 International Conference on Information Management, Innovation Management and Industrial Engineering - Volume 03
Comparing parameter tuning methods for evolutionary algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Paper: The parallel genetic algorithm as function optimizer
Parallel Computing
Network crossover performance on NK landscapes and deceptive problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
An exploration into dynamic population sizing
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolving crossover operators for function optimization
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Using supportive coevolution to evolve self-configuring crossover
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Evolving black-box search algorithms employing genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Crossover is a core genetic operator in many evolutionary algorithms (EAs). The performance of such EAs on a given problem is dependent on properly configuring crossover. A small set of common crossover operators is used in the vast majority of EAs, typically fixed for the entire evolutionary run. Selecting which crossover operator to use and tuning its associated parameters to obtain acceptable performance on a specific problem often is a time consuming manual process. Even then a custom crossover operator may be required to achieve optimal performance. Finally, the best crossover configuration may be dependent on the state of the evolutionary run. This paper introduces the Self-Configuring Crossover operator encoded with linear genetic programming which addresses these shortcomings while relieving the user from the burden of crossover configuration. To demonstrate its general applicability, the novel crossover operator was applied without any problem specific tuning. Results are presented showing it to outperform the traditional crossover operators arithmetic crossover, uniform crossover, and n-point crossover on the Rosenbrock, Rastrigin, Offset Rastrigin, DTrap, and NK Landscapes benchmark problems.