A First Step towards the Runtime Analysis of Evolutionary Algorithm Adjusted with Reinforcement Learning.

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

Crynodeb

A first step towards analyzing runtime complexity of an evolutionary algorithm adaptively adjusted using reinforcement learning is made. We analyze the previously proposed EA + RL method that enhances single-objective optimization by selecting efficient auxiliary fitness functions. Precisely, Random Mutation Hill Climber adjusted with Q-learning using greedy exploration strategy is considered. We obtain both lower and upper bound for the number of fitness function evaluations needed for this EA + RL implementation to solve a modified OneMax problem. It turns out that EA + RL with an inefficient auxiliary fitness function performs on par with a conventional evolutionary algorithm, namely in Θ(N log N) fitness function evaluations, where N is the size of the OneMax problem. In other words, we show that reinforcement learning successfully ignores inefficient fitness function. A lower bound for the ε-greedy exploration strategy for ε > 0 is analyzed as well.
Iaith wreiddiolSaesneg
TeitlICMLA '13
Is-deitlProceedings of the 2013 12th International Conference on Machine Learning and Applications
CyhoeddwrIEEE Press
Tudalennau203-208
Nifer y tudalennau6
Cyfrol1
ISBN (Electronig)978-0-7695-5144-9
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 04 Rhag 2013
Cyhoeddwyd yn allanolIe
Digwyddiad2013 12th International Conference on Machine Learning and Applications - Miami, Unol Daleithiau America
Hyd: 04 Rhag 201307 Rhag 2013

Cynhadledd

Cynhadledd2013 12th International Conference on Machine Learning and Applications
Gwlad/TiriogaethUnol Daleithiau America
DinasMiami
Cyfnod04 Rhag 201307 Rhag 2013

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