A preliminary study of learnable evolution methodology implemented with C4.5

Mark Coletti

doi:10.1109/CEC.2002.1006992

A preliminary study of learnable evolution methodology implemented with C4.5

Mark Coletti

Research output: Contribution to conference › Paper › peer-review

2 Scopus citations

Abstract

The learnable evolution model (LEM) introduces a machine learning-based birth operator into an evolutionary computing algorithm. New individuals are generated from hypotheses learned by the operator from the most-fit and least-fit parent sub-populations. The LEM allows for arbitrary machine learning mechanisms, though, so far, only an AQ (Algorithm Quasi-optimal) based machine learner has been used in LEM implementations. This paper describes preliminary results using a different machine learner in a LEM implementation - C4.5.

Original language	English
Pages	588-593
Number of pages	6
DOIs	https://doi.org/10.1109/CEC.2002.1006992
State	Published - 2002
Externally published	Yes
Event	2002 Congress on Evolutionary Computation, CEC 2002 - Honolulu, HI, United States Duration: May 12 2002 → May 17 2002

Conference

Conference	2002 Congress on Evolutionary Computation, CEC 2002
Country/Territory	United States
City	Honolulu, HI
Period	05/12/02 → 05/17/02

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10.1109/CEC.2002.1006992

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abstract = "The learnable evolution model (LEM) introduces a machine learning-based birth operator into an evolutionary computing algorithm. New individuals are generated from hypotheses learned by the operator from the most-fit and least-fit parent sub-populations. The LEM allows for arbitrary machine learning mechanisms, though, so far, only an AQ (Algorithm Quasi-optimal) based machine learner has been used in LEM implementations. This paper describes preliminary results using a different machine learner in a LEM implementation - C4.5.",

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AB - The learnable evolution model (LEM) introduces a machine learning-based birth operator into an evolutionary computing algorithm. New individuals are generated from hypotheses learned by the operator from the most-fit and least-fit parent sub-populations. The LEM allows for arbitrary machine learning mechanisms, though, so far, only an AQ (Algorithm Quasi-optimal) based machine learner has been used in LEM implementations. This paper describes preliminary results using a different machine learner in a LEM implementation - C4.5.

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T2 - 2002 Congress on Evolutionary Computation, CEC 2002

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ER -

A preliminary study of learnable evolution methodology implemented with C4.5

Abstract

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