Diversity-Guided Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
Use of statistical outlier detection method in adaptive evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
GASAT: a genetic local search algorithm for the satisfiability problem
Evolutionary Computation
Forking genetic algorithms: Gas with search space division schemes
Evolutionary Computation
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
Reinforcement learning for online control of evolutionary algorithms
ESOA'06 Proceedings of the 4th international conference on Engineering self-organising systems
Autonomous operator management for evolutionary algorithms
Journal of Heuristics
On-the-fly calibrating strategies for evolutionary algorithms
Information Sciences: an International Journal
Analyzing bandit-based adaptive operator selection mechanisms
Annals of Mathematics and Artificial Intelligence
From adaptive to more dynamic control in evolutionary algorithms
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
Pareto autonomous local search
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Adaptive evolutionary algorithms and extensions to the hyflex hyper-heuristic framework
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
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Parameter control is a key issue to enhance performances of Genetic Algorithms (GA). Although many studies exist on this problem, it is rarely addressed in a general way. Consequently, in practice, parameters are often adjusted manually. Some generic approaches have been experimented by looking at the recent improvements provided by the operators. In this paper, we extend this approach by including operators' effect over population diversity and computation time. Our controller, named Compass, provides an abstraction of GA's parameters that allows the user to directly adjust the balance between exploration and exploitation of the search space. The approach is then experimented on the resolution of a classic combinatorial problem (SAT).