research-article

A novel probabilistic encoding for EAs applied to biclustering of microarray data

Authors:

Michaël Marcozzi,

Federico Divina,

Jesús S. Aguilar-Ruiz,

Wim VanhoofAuthors Info & Claims

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

Pages 339 - 346

https://doi.org/10.1145/2001576.2001623

Published: 12 July 2011 Publication History

Abstract

In this paper we propose a novel representation scheme, called probabilistic encoding. In this representation, each gene of an individual represents the probability that a certain trait of a given problem has to belong to the solution. This allows to deal with uncertainty that can be present in an optimization problem, and grant more exploration capability to an evolutionary algorithm. With this encoding, the search is not restricted to points of the search space. Instead, whole regions are searched, with the aim of individuating a promising region, i.e., a region that contains the optimal solution. This implies that a strategy for searching the individuated region has to be adopted. In this paper we incorporate the probabilistic encoding into a multi-objective and multi-modal evolutionary algorithm. The algorithm returns a promising region, which is then searched by using simulated annealing. We apply our proposal to the problem of discovering biclusters in microarray data. Results confirm the validity of our proposal.

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Cited By

Baron SLazzarini NBacardit J(2017)Characterising the Influence of Rule-Based Knowledge Representations in Biological Knowledge Extraction from Transcriptomics DataApplications of Evolutionary Computation10.1007/978-3-319-55849-3_9(125-141)Online publication date: 25-Mar-2017
https://doi.org/10.1007/978-3-319-55849-3_9
Gil-Cumbreras FGiráldez RAguilar-Ruiz J(2016)Extending Probabilistic Encoding for Discovering Biclusters in Gene Expression DataHybrid Artificial Intelligent Systems10.1007/978-3-319-32034-2_59(706-717)Online publication date: 14-Apr-2016
https://doi.org/10.1007/978-3-319-32034-2_59

Index Terms

A novel probabilistic encoding for EAs applied to biclustering of microarray data
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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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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SIGEVO

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

Baron SLazzarini NBacardit J(2017)Characterising the Influence of Rule-Based Knowledge Representations in Biological Knowledge Extraction from Transcriptomics DataApplications of Evolutionary Computation10.1007/978-3-319-55849-3_9(125-141)Online publication date: 25-Mar-2017
https://doi.org/10.1007/978-3-319-55849-3_9
Gil-Cumbreras FGiráldez RAguilar-Ruiz J(2016)Extending Probabilistic Encoding for Discovering Biclusters in Gene Expression DataHybrid Artificial Intelligent Systems10.1007/978-3-319-32034-2_59(706-717)Online publication date: 14-Apr-2016
https://doi.org/10.1007/978-3-319-32034-2_59

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