Exploration of Ontological Representations for Evolutionary Computation

Hugo I Alcaraz Herrera; John P Cartlidge

doi:10.1109/CEC55065.2022.9870376

Exploration of Ontological Representations for Evolutionary Computation

Hugo I Alcaraz Herrera^*
, John P Cartlidge

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

4 Citations (Scopus)

130 Downloads (Pure)

Abstract

This research explores the utility of ontological representations using object-oriented (OO) design principles, such that characteristics of the problem domain are directly mapped onto the representation of individuals. A comparison against more traditional representations is performed in two problem domains of differing complexity: (i) Tangram, a simple geometric puzzle; and (ii) EvoRecSys, an evolutionary recommender system for health and well-being advice. We show that OO representations aid research and development as naturally decoupled components can be more easily modified and extended, which can in turn lead to the discovery of better solutions.

Original language	English
Title of host publication	2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Number of pages	8
ISBN (Electronic)	9781665467087
ISBN (Print)	9781665467094
DOIs	https://doi.org/10.1109/CEC55065.2022.9870376
Publication status	Published - 6 Sept 2022
Event	IEEE Congress on Evolutionary Computation - Padua, Italy Duration: 18 Jul 2022 → 23 Jul 2022 https://wcci2022.org/

Conference

Conference	IEEE Congress on Evolutionary Computation
Abbreviated title	CEC
Country/Territory	Italy
City	Padua
Period	18/07/22 → 23/07/22
Internet address	https://wcci2022.org/

Bibliographical note

Funding Information:
Hugo Alcaraz-Herrera’s PhD is supported by The Mexican Council of Science and Technology (Consejo Nacional de Ciencia y Tecnología - CONACyT). John Cartlidge is sponsored by Refinitiv.

Publisher Copyright:
© 2022 IEEE.

Keywords

evolutionary computation
Genetic algorithms

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10.1109/CEC55065.2022.9870376

Full-text PDF (accepted manuscript)
This is the accepted author manuscript (AAM). The final published version (version of record) is available online via IEEE at https://doi.org/10.1109/CEC55065.2022.9870376. Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 1.64 MB

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Studies on complex representations for evolutionary computation and mitigation techniques for pathologies observed in coevolutionary computation
Alcaraz Herrera, H. I. (Author), Cartlidge, J. (Supervisor) & Cliff, D. (Supervisor), 3 Oct 2023
Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)

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title = "Exploration of Ontological Representations for Evolutionary Computation",

abstract = "This research explores the utility of ontological representations using object-oriented (OO) design principles, such that characteristics of the problem domain are directly mapped onto the representation of individuals. A comparison against more traditional representations is performed in two problem domains of differing complexity: (i) Tangram, a simple geometric puzzle; and (ii) EvoRecSys, an evolutionary recommender system for health and well-being advice. We show that OO representations aid research and development as naturally decoupled components can be more easily modified and extended, which can in turn lead to the discovery of better solutions.",

keywords = "evolutionary computation, Genetic algorithms",

author = "\{Alcaraz Herrera\}, \{Hugo I\} and Cartlidge, \{John P\}",

note = "Funding Information: Hugo Alcaraz-Herrera{\textquoteright}s PhD is supported by The Mexican Council of Science and Technology (Consejo Nacional de Ciencia y Tecnolog{\'i}a - CONACyT). John Cartlidge is sponsored by Refinitiv. Publisher Copyright: {\textcopyright} 2022 IEEE.; IEEE Congress on Evolutionary Computation , CEC ; Conference date: 18-07-2022 Through 23-07-2022",

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language = "English",

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Alcaraz Herrera, HI & Cartlidge, JP 2022, Exploration of Ontological Representations for Evolutionary Computation. in 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings., 22027227, Institute of Electrical and Electronics Engineers (IEEE), IEEE Congress on Evolutionary Computation , Padua, Italy, 18/07/22. https://doi.org/10.1109/CEC55065.2022.9870376

Exploration of Ontological Representations for Evolutionary Computation. / Alcaraz Herrera, Hugo I ; Cartlidge, John P.
2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings. Institute of Electrical and Electronics Engineers (IEEE), 2022. 22027227.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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AU - Cartlidge, John P

N1 - Funding Information: Hugo Alcaraz-Herrera’s PhD is supported by The Mexican Council of Science and Technology (Consejo Nacional de Ciencia y Tecnología - CONACyT). John Cartlidge is sponsored by Refinitiv. Publisher Copyright: © 2022 IEEE.

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AB - This research explores the utility of ontological representations using object-oriented (OO) design principles, such that characteristics of the problem domain are directly mapped onto the representation of individuals. A comparison against more traditional representations is performed in two problem domains of differing complexity: (i) Tangram, a simple geometric puzzle; and (ii) EvoRecSys, an evolutionary recommender system for health and well-being advice. We show that OO representations aid research and development as naturally decoupled components can be more easily modified and extended, which can in turn lead to the discovery of better solutions.

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KW - Genetic algorithms

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M3 - Conference Contribution (Conference Proceeding)

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T2 - IEEE Congress on Evolutionary Computation

Y2 - 18 July 2022 through 23 July 2022

ER -

Exploration of Ontological Representations for Evolutionary Computation

Abstract

Conference

Bibliographical note

Keywords

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Student theses

Studies on complex representations for evolutionary computation and mitigation techniques for pathologies observed in coevolutionary computation

Cite this