research-article

Modelling the initialisation stage of the ALKR representation for discrete domains and GABIL encoding

Authors:

Maria A. Franco,

Natalio Krasnogor,

Jaume BacarditAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

Pages 1291 - 1298

https://doi.org/10.1145/2001576.2001750

Published: 12 July 2011 Publication History

Abstract

Models in Genetic Based Machine Learning (GBML) systems are commonly used to gain understanding of how the system works and, as a consequence, adjust it better. In this paper we propose models for the probability of having a good initial population using the Attribute List Knowledge Representation (ALKR) for discrete inputs using the GABIL encoding. We base our work in the schema and covering bound models previously proposed for XCS. The models are extended to (a) deal with the combination of ALKR+GABIL representation, (b) explicitly handle datasets with niche overlap and (c) model the impact of using covering and a default rule in the representation. The models are designed and evaluated within the framework of the BioHEL GBML system and are empirically evaluated using first boolean datasets and later also nominal datasets of higher cardinality. The models in this paper allow us to evaluate the challenges presented by problems with high cardinality (in terms of number of attributes and values of the attributes) as well as the benefits contributed by each of the components of BioHEL's representation and initialisation operators.

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Cited By

Franco MKrasnogor NBacardit J(2020)Automatic Tuning of Rule-Based Evolutionary Machine Learning via Problem Structure IdentificationIEEE Computational Intelligence Magazine10.1109/MCI.2020.299823215:3(28-46)Online publication date: Aug-2020
https://doi.org/10.1109/MCI.2020.2998232
Bacardit JLlorà XAlba E(2013)Large scale data mining using genetics-based machine learningProceedings of the 15th annual conference companion on Genetic and evolutionary computation10.1145/2464576.2480807(741-764)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2464576.2480807
Bacardit JLlorà XMoore J(2012)Large scale data mining using genetics-based machine learningProceedings of the 14th annual conference companion on Genetic and evolutionary computation10.1145/2330784.2330936(1171-1196)Online publication date: 7-Jul-2012
https://dl.acm.org/doi/10.1145/2330784.2330936
Show More Cited By

Index Terms

Modelling the initialisation stage of the ALKR representation for discrete domains and GABIL encoding
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Logical and relational learning
        Inductive logic learning
2. Theory of computation
  1. Design and analysis of algorithms

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cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2011

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GECCO '11

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GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Franco MKrasnogor NBacardit J(2020)Automatic Tuning of Rule-Based Evolutionary Machine Learning via Problem Structure IdentificationIEEE Computational Intelligence Magazine10.1109/MCI.2020.299823215:3(28-46)Online publication date: Aug-2020
https://doi.org/10.1109/MCI.2020.2998232
Bacardit JLlorà XAlba E(2013)Large scale data mining using genetics-based machine learningProceedings of the 15th annual conference companion on Genetic and evolutionary computation10.1145/2464576.2480807(741-764)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2464576.2480807
Bacardit JLlorà XMoore J(2012)Large scale data mining using genetics-based machine learningProceedings of the 14th annual conference companion on Genetic and evolutionary computation10.1145/2330784.2330936(1171-1196)Online publication date: 7-Jul-2012
https://dl.acm.org/doi/10.1145/2330784.2330936
Franco MKrasnogor NBacardit J(2012)Analysing BioHEL using challenging boolean functionsEvolutionary Intelligence10.1007/s12065-012-0080-95:2(87-102)Online publication date: 22-May-2012
https://doi.org/10.1007/s12065-012-0080-9
Bacardit JLlorà XLanzi P(2011)Large scale data mining using genetics-based machine learningProceedings of the 13th annual conference companion on Genetic and evolutionary computation10.1145/2001858.2002137(1285-1310)Online publication date: 12-Jul-2011
https://dl.acm.org/doi/10.1145/2001858.2002137

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