research-article

Enhancing search space diversity in multi-objective evolutionary drug molecule design using niching

Authors:

Johannes W. Kruisselbrink,

Alexander Aleman,

Michael T.M. Emmerich,

Ad P. IJzerman,

Andreas Bender,

Eelke van der HorstAuthors Info & Claims

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

Pages 217 - 224

https://doi.org/10.1145/1569901.1569932

Published: 08 July 2009 Publication History

Abstract

There exist several applications of multi-objective evolutionary algorithms for drug design, however, a common drawback in recent approaches is that the diversity of resulting molecule populations is relatively low. This paper seeks to overcome this problem by introducing niching as a technique to enhance search space diversity. A single population approach with dynamic niche identification is studied in the application domain. In order to apply niching in molecular spaces a metric for measuring the dissimilarity of molecules will be introduced. The approach will be validated in case studies and compared with results of an NSGA-II algorithm without niching in the search space.

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Index Terms

Enhancing search space diversity in multi-objective evolutionary drug molecule design using niching
1. Applied computing
  1. Physical sciences and engineering
    1. Chemistry

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cover image ACM Conferences

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

July 2009

2036 pages

ISBN:9781605583259

DOI:10.1145/1569901

General Chair:
Franz Rothlauf
University of Mainz, Germany

Copyright © 2009 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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Published: 08 July 2009

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GECCO09

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GECCO09: Genetic and Evolutionary Computation Conference

July 8 - 12, 2009

Québec, Montreal, Canada

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

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Liu YXu LHan YZeng XYen GIshibuchi H(2024)Evolutionary Multimodal Multiobjective Optimization for Traveling Salesman ProblemsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.323954628:2(516-530)Online publication date: Apr-2024
https://doi.org/10.1109/TEVC.2023.3239546
Ray ADas PBhattacharyya SPlatos JMrsic LHassanien A(2024)A Brief Review on Quantum Drug DesignBusiness Intelligence and Information Technology10.1007/978-981-97-3980-6_41(475-483)Online publication date: 30-Aug-2024
https://doi.org/10.1007/978-981-97-3980-6_41
Ji XZhang YGong DSun XGuo Y(2023)Multisurrogate-Assisted Multitasking Particle Swarm Optimization for Expensive Multimodal ProblemsIEEE Transactions on Cybernetics10.1109/TCYB.2021.312362553:4(2516-2530)Online publication date: Apr-2023
https://doi.org/10.1109/TCYB.2021.3123625
Liang JLi ZSun ZBi YCheng HZeng TGuo W(2023)Latent space search based multimodal optimization with personalized edge-network biomarker for multi-purpose early disease predictionBriefings in Bioinformatics10.1093/bib/bbad36424:6Online publication date: 13-Oct-2023
https://doi.org/10.1093/bib/bbad364
Zhao HZhan ZLiu J(2023)Outlier aware differential evolution for multimodal optimization problemsApplied Soft Computing10.1016/j.asoc.2023.110264140(110264)Online publication date: Jun-2023
https://doi.org/10.1016/j.asoc.2023.110264
Zeng LChiang HLiang DXia MDong N(2022)Trust-Tech Source-Point Method for Systematically Computing Multiple Local Optimal Solutions: Theory and MethodIEEE Transactions on Cybernetics10.1109/TCYB.2021.307146252:11(11686-11697)Online publication date: Nov-2022
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Liang JLi ZYue CHu ZCheng HLiu ZGuo W(2022)Multi-modal optimization to identify personalized biomarkers for disease prediction of individual patients with cancerBriefings in Bioinformatics10.1093/bib/bbac25423:5Online publication date: 21-Jul-2022
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Ji XZhang YGong DSun X(2021)Dual-Surrogate-Assisted Cooperative Particle Swarm Optimization for Expensive Multimodal ProblemsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.306483525:4(794-808)Online publication date: Aug-2021
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Wu YPeng XXu D(2019)Identifying Variables Interaction for Black-box Continuous Optimization with Mutual Information of Multiple Local Optima2019 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI44817.2019.9003021(2683-2689)Online publication date: Dec-2019
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Li YYu JTakagi H(2019)Niche Method Complementing the Nearest-better Clustering2019 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI44817.2019.9002742(3065-3071)Online publication date: Dec-2019
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