Choosing Best Fitness Function with Reinforcement Learning

Arina Buzdalova; Maxim Buzdalov

doi:10.1109/ICMLA.2011.163

Choosing Best Fitness Function with Reinforcement Learning

Arina Buzdalova, Maxim Buzdalov

ITMO University

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (ISBN)

11 Citations (Scopus)

Abstract

This paper describes an optimization problem with one target function to be optimized and several supporting functions that can be used to speed up the optimization process. A method based on reinforcement learning is proposed for choosing a good supporting function during optimization using genetic algorithm. Results of applying this method to a model problem are shown.

Original language	English
Title of host publication	ICMLA '11
Subtitle of host publication	Proceedings of the 2011 10th International Conference on Machine Learning and Applications and Workshops
Publisher	IEEE Press
Pages	354-357
Number of pages	4
Volume	2
ISBN (Print)	978-1-4577-2134-2
DOIs	https://doi.org/10.1109/ICMLA.2011.163
Publication status	Published - 11 Dec 2011
Externally published	Yes

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title = "Choosing Best Fitness Function with Reinforcement Learning",

abstract = "This paper describes an optimization problem with one target function to be optimized and several supporting functions that can be used to speed up the optimization process. A method based on reinforcement learning is proposed for choosing a good supporting function during optimization using genetic algorithm. Results of applying this method to a model problem are shown. ",

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AB - This paper describes an optimization problem with one target function to be optimized and several supporting functions that can be used to speed up the optimization process. A method based on reinforcement learning is proposed for choosing a good supporting function during optimization using genetic algorithm. Results of applying this method to a model problem are shown.

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BT - ICMLA '11

PB - IEEE Press

ER -

Choosing Best Fitness Function with Reinforcement Learning

Abstract

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