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Output details

11 - Computer Science and Informatics

University of Kent

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Output 7 of 117 in the submission

Output title

A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms

Type

E - Conference contribution

DOI

10.1145/2330163.2330335

Name of conference/published proceedings

GECCO '12 Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference

Volume number

Issue number

First page of article

1237

ISSN of proceedings

Year of publication

2012

URL

Number of additional authors

Additional information

<24> This paper proposes the first evolutionary algorithm for automatically designing a full decision tree induction algorithm, inaugurating a new research topic at the intersection of evolutionary algorithms and decision tree induction. The automatically-designed decision tree algorithm has outperformed (with statistical significance) two very popular manually designed decision-tree algorithms (C4.5 and CART) across 20 classification datasets. This paper received the best paper award of three tracks of the ACM GECCO-2012 conference (flagship conference on evolutionary algorithms): Integrative Genetic and Evolutionary Computation, Self-* Search and Search-based Software Engineering, and an invited extended version is in press in the Evolutionary Computation journal.

Interdisciplinary

Cross-referral requested

Research group

I - Computational Intelligence Group

Citation count

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract