A New Reduced-Length Genetic Representation for Evolutionary Multiobjective Clustering

Mario Garza-Fabre; Julia Handl; Joshua Knowles

doi:10.1007/978-3-319-54157-0_17

A New Reduced-Length Genetic Representation for Evolutionary Multiobjective Clustering

Mario Garza-Fabre, Julia Handl, Joshua Knowles

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

196 Downloads (Pure)

Abstract

The last decade has seen a growing body of research illustrating the advantages of the evolutionary multiobjective approach to data clustering. The scalability of such an approach, however, is a topic which merits more attention given the unprecedented volumes of data generated nowadays. This paper proposes a reduced-length representation for evolutionary multiobjective clustering. The new encoding explicitly prunes the solution space and allows the search method to focus on its most promising regions. Moreover, it allows us to precompute information in order to alleviate the computational overhead caused by the processing of candidate individuals during optimisation. We investigate the suitability of this proposal in the context of a representative algorithm from
the literature: MOCK. Our results indicate that the new reduced-length
representation significantly improves the effectiveness and computational
efficiency of MOCK specifically, and can be seen as a further step towards a better scalability of evolutionary multiobjective clustering in general.

Original language	English
Title of host publication	Evolutionary Multi-Criterion Optimization
Subtitle of host publication	9th International Conference, EMO 2017, Munster, Germany, March 19 - March 22, 2017, Proceedings
Publisher	Springer
Pages	236-251
Number of pages	15
Volume	10173
ISBN (Electronic)	978-3-319-54157-0
ISBN (Print)	978-3-319-54156-3
DOIs	https://doi.org/10.1007/978-3-319-54157-0_17
Publication status	Published - 2017
Event	9th International Conference on Evolutionary Multi-Criterion Optimization - Munster, Germany Duration: 19 Mar 2017 → 22 Mar 2017

Publication series

Name	Lecture Notes in Computer Science

Conference

Conference	9th International Conference on Evolutionary Multi-Criterion Optimization
Country/Territory	Germany
City	Munster
Period	19/03/17 → 22/03/17

Keywords

Data Clustering
Evolutionary
Multiobjective Optimisation
Genetic Representation
Scalability

Access to Document

10.1007/978-3-319-54157-0_17

Mario_paper_29
Checked 26/1/2017
Accepted author manuscript, 662 KBLicence: None: All rights reserved

Cite this

Garza-Fabre, M., Handl, J., & Knowles, J. (2017). A New Reduced-Length Genetic Representation for Evolutionary Multiobjective Clustering. In Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Munster, Germany, March 19 - March 22, 2017, Proceedings (Vol. 10173, pp. 236-251). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-319-54157-0_17

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title = "A New Reduced-Length Genetic Representation for Evolutionary Multiobjective Clustering",

abstract = "The last decade has seen a growing body of research illustrating the advantages of the evolutionary multiobjective approach to data clustering. The scalability of such an approach, however, is a topic which merits more attention given the unprecedented volumes of data generated nowadays. This paper proposes a reduced-length representation for evolutionary multiobjective clustering. The new encoding explicitly prunes the solution space and allows the search method to focus on its most promising regions. Moreover, it allows us to precompute information in order to alleviate the computational overhead caused by the processing of candidate individuals during optimisation. We investigate the suitability of this proposal in the context of a representative algorithm fromthe literature: MOCK. Our results indicate that the new reduced-lengthrepresentation significantly improves the effectiveness and computationalefficiency of MOCK specifically, and can be seen as a further step towards a better scalability of evolutionary multiobjective clustering in general.",

keywords = "Data Clustering , Evolutionary , Multiobjective Optimisation , Genetic Representation , Scalability",

author = "Mario Garza-Fabre and Julia Handl and Joshua Knowles",

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Garza-Fabre, M, Handl, J & Knowles, J 2017, A New Reduced-Length Genetic Representation for Evolutionary Multiobjective Clustering. in Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Munster, Germany, March 19 - March 22, 2017, Proceedings. vol. 10173, Lecture Notes in Computer Science, Springer, pp. 236-251, 9th International Conference on Evolutionary Multi-Criterion Optimization, Munster, Germany, 19/03/17. https://doi.org/10.1007/978-3-319-54157-0_17

A New Reduced-Length Genetic Representation for Evolutionary Multiobjective Clustering. / Garza-Fabre, Mario; Handl, Julia; Knowles, Joshua.
Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Munster, Germany, March 19 - March 22, 2017, Proceedings. Vol. 10173 Springer, 2017. p. 236-251 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A New Reduced-Length Genetic Representation for Evolutionary Multiobjective Clustering

AU - Garza-Fabre, Mario

AU - Handl, Julia

AU - Knowles, Joshua

PY - 2017

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N2 - The last decade has seen a growing body of research illustrating the advantages of the evolutionary multiobjective approach to data clustering. The scalability of such an approach, however, is a topic which merits more attention given the unprecedented volumes of data generated nowadays. This paper proposes a reduced-length representation for evolutionary multiobjective clustering. The new encoding explicitly prunes the solution space and allows the search method to focus on its most promising regions. Moreover, it allows us to precompute information in order to alleviate the computational overhead caused by the processing of candidate individuals during optimisation. We investigate the suitability of this proposal in the context of a representative algorithm fromthe literature: MOCK. Our results indicate that the new reduced-lengthrepresentation significantly improves the effectiveness and computationalefficiency of MOCK specifically, and can be seen as a further step towards a better scalability of evolutionary multiobjective clustering in general.

AB - The last decade has seen a growing body of research illustrating the advantages of the evolutionary multiobjective approach to data clustering. The scalability of such an approach, however, is a topic which merits more attention given the unprecedented volumes of data generated nowadays. This paper proposes a reduced-length representation for evolutionary multiobjective clustering. The new encoding explicitly prunes the solution space and allows the search method to focus on its most promising regions. Moreover, it allows us to precompute information in order to alleviate the computational overhead caused by the processing of candidate individuals during optimisation. We investigate the suitability of this proposal in the context of a representative algorithm fromthe literature: MOCK. Our results indicate that the new reduced-lengthrepresentation significantly improves the effectiveness and computationalefficiency of MOCK specifically, and can be seen as a further step towards a better scalability of evolutionary multiobjective clustering in general.

KW - Data Clustering

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KW - Multiobjective Optimisation

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KW - Scalability

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Garza-Fabre M, Handl J, Knowles J. A New Reduced-Length Genetic Representation for Evolutionary Multiobjective Clustering. In Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Munster, Germany, March 19 - March 22, 2017, Proceedings. Vol. 10173. Springer. 2017. p. 236-251. (Lecture Notes in Computer Science). Epub 2017 Feb 19. doi: 10.1007/978-3-319-54157-0_17

A New Reduced-Length Genetic Representation for Evolutionary Multiobjective Clustering

Abstract

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