Multiagent learning using a variable learning rate
Artificial Intelligence
Evolution of Appropriate Crossover and Mutation Operators in a Genetic Process
Applied Intelligence
Simultaneously Applying Multiple Mutation Operators in Genetic Algorithms
Journal of Heuristics
Theoretical Computer Science - Natural computing
Convergence of Gradient Dynamics with a Variable Learning Rate
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Nash Convergence of Gradient Dynamics in General-Sum Games
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
The Journal of Machine Learning Research
A Hybrid Metaheuristic for the Quadratic Assignment Problem
Computational Optimization and Applications
On the convergence of stochastic iterative dynamic programming algorithms
Neural Computation
Using multiple offspring sampling to guide genetic algorithms to solve permutation problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Hybrid evolutionary algorithms for large scale continuous problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Tentative Exploration on Reinforcement Learning Algorithms for Stochastic Rewards
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
A multiagent reinforcement learning algorithm with non-linear dynamics
Journal of Artificial Intelligence Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Rational and convergent learning in stochastic games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
An improved GA and a novel PSO-GA-based hybrid algorithm
Information Processing Letters
Reinforcement learning for online control of evolutionary algorithms
ESOA'06 Proceedings of the 4th international conference on Engineering self-organising systems
Negative slope coefficient: a measure to characterize genetic programming fitness landscapes
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A new methodology for the automatic creation of adaptive hybrid algorithms
Intelligent Data Analysis
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Evolutionary Algorithms are powerful optimization techniques which have been applied to many different problems, from complex mathematical functions to real-world applications. Some studies report performance improvements through the combination of different evolutionary approaches within the same hybrid algorithm. However, the mechanisms used to control this combination of evolutionary approaches are not as satisfactory as would be desirable. In most cases, there is no feedback from the algorithm nor any regulatory component that modifies the participation of each evolutionary approach in the overall search process. In some cases, the algorithm makes use of some information for an on-line adaptation of the participation of each algorithm. In this paper, the use of Reinforcement Learning (RL) is proposed as a mechanism to control how the different evolutionary approaches contribute to the overall search process. In particular, three learning policies based on one of the state-of-the-art RL algorithms, Q-Learning, have been considered and used to control the participation of each algorithm by learning the best-response mixed strategy. To test this approach, a benchmark made up of six large-scale (500 dimensions) continuous optimization functions has been considered. The experimentation carried out has proved that RL control mechanisms successfully learn optimal patterns for the combination of Evolutionary Algorithms in most of the proposed functions, being able to improve the performance of both individual and non RL hybrid algorithms.