Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Adaptive operator selection with dynamic multi-armed bandits
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A reinforcement learning approach to job-shop scheduling
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Reinforcement learning for online control of evolutionary algorithms
ESOA'06 Proceedings of the 4th international conference on Engineering self-organising systems
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
On handling ephemeral resource constraints in evolutionary search
Evolutionary Computation
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We consider an optimization scenario in which resources are required in the evaluation process of candidate solutions. The challenge we are focussing on is that certain resources have to be committed to for some period of time whenever they are used by an optimizer. This has the effect that certain solutions may be temporarily non-evaluable during the optimization. Previous analysis revealed that evolutionary algorithms (EAs) can be effective against this resourcing issue when augmented with static strategies for dealing with non-evaluable solutions, such as repairing, waiting, or penalty methods. Moreover, it is possible to select a suitable strategy for resource-constrained problems offline if the resourcing issue is known in advance. In this paper we demonstrate that an EA that uses a reinforcement learning (RL) agent, here Sarsa(λ), to learn offline when to switch between static strategies, can be more effective than any of the static strategies themselves. We also show that learning the same task as the RL agent but online using an adaptive strategy selection method, here D-MAB, is not as effective; nevertheless, online learning is an alternative to static strategies.