Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
A study of permutation crossover operators on the traveling salesman problem
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Proceedings of the third international conference on Genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
The Estimation of Distributions and the Minimum Relative Entropy Principle
Evolutionary Computation
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
Relevance estimation and value calibration of evolutionary algorithm parameters
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A general-purpose tunable landscape generator
IEEE Transactions on Evolutionary Computation
Autonomous operator management for evolutionary algorithms
Journal of Heuristics
A dynamic Island-based genetic algorithms framework
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
A parameter-less genetic algorithm with customized crossover and mutation operators
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Instance-based parameter tuning for evolutionary AI planning
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
From adaptive to more dynamic control in evolutionary algorithms
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
Parameter tuning of evolutionary algorithms: generalist vs. specialist
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
A generic approach to parameter control
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Parameter tuning of evolutionary reactions systems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
An on-line on-board distributed algorithm for evolutionary robotics
EA'11 Proceedings of the 10th international conference on Artificial Evolution
Learn-and-Optimize: a parameter tuning framework for evolutionary AI planning
EA'11 Proceedings of the 10th international conference on Artificial Evolution
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We present an empirical study on the impact of different design choices on the performance of an evolutionary algorithm (EA). Four EA components are considered--parent selection, survivor selection, recombination and mutation--and for each component we study the impact of choosing the right operator, and of tuning its free parameter(s). We tune 120 different combinations of EA operators to 4 different classes of fitness landscapes, and measure the cost of tuning. We find that components differ greatly in importance. Typically the choice of operator for parent selection has the greatest impact, and mutation needs the most tuning. Regarding individual EAs however, the impact of design choices for one component depends on the choices for other components, as well as on the available amount of resources for tuning.