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Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization.

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Title: Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization.
Authors: Santoni, Maria Laura1 (AUTHOR) maria-laura.santoni@lip6.fr, Raponi, Elena2 (AUTHOR) e.raponi@liacs.leidenuniv.nl, Neumann, Aneta3 (AUTHOR) aneta.neumann@adelaide.edu.au, Neumann, Frank3 (AUTHOR) frank.neumann@adelaide.edu.au, Preuss, Mike2 (AUTHOR) m.preuss@liacs.leidenuniv.nl, Doerr, Carola1 (AUTHOR) carola.doerr@lip6.fr
Source: Evolutionary Computation. Summer 2026, Vol. 34 Issue 2, p213-233. 21p.
Subjects: Subset selection, Heuristic, Metaheuristic algorithms, Statistical sampling, Mathematical optimization, Excellence
Abstract: In real-world applications, users often favor structurally diverse design choices over one high-quality solution. It is therefore important to consider more solutions that decision makers can compare and further explore based on additional criteria. Alongside the existing approaches of evolutionary diversity optimization, quality diversity, and multimodal optimization, this paper presents a fresh perspective on this challenge by considering the problem of identifying a fixed number of solutions with a pairwise distance above a specified threshold while maximizing their average quality. We obtain first insight into these objectives by performing a subset selection on the search trajectories of different well-established search heuristics, whether they have been specifically designed with diversity in mind or not. We emphasize that the main goal of our work is not to present a new algorithm but to understand the capability of off-the-shelf algorithms to quantify the trade-off between the minimum pairwise distance within batches of solutions and their average quality. We also analyze how this trade-off depends on the properties of the underlying optimization problem. A possibly surprising outcome of our empirical study is the observation that naive uniform random sampling establishes a very strong baseline for our problem, hardly ever outperformed by the search trajectories of the considered heuristics. We interpret these results as a motivation to develop algorithms tailored to produce diverse solutions of high average quality. [ABSTRACT FROM AUTHOR]
Copyright of Evolutionary Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 194184279
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PubTypeId: academicJournal
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  Data: Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization.
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  Data: <searchLink fieldCode="AR" term="%22Santoni%2C+Maria+Laura%22">Santoni, Maria Laura</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> maria-laura.santoni@lip6.fr</i><br /><searchLink fieldCode="AR" term="%22Raponi%2C+Elena%22">Raponi, Elena</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> e.raponi@liacs.leidenuniv.nl</i><br /><searchLink fieldCode="AR" term="%22Neumann%2C+Aneta%22">Neumann, Aneta</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> aneta.neumann@adelaide.edu.au</i><br /><searchLink fieldCode="AR" term="%22Neumann%2C+Frank%22">Neumann, Frank</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> frank.neumann@adelaide.edu.au</i><br /><searchLink fieldCode="AR" term="%22Preuss%2C+Mike%22">Preuss, Mike</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> m.preuss@liacs.leidenuniv.nl</i><br /><searchLink fieldCode="AR" term="%22Doerr%2C+Carola%22">Doerr, Carola</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> carola.doerr@lip6.fr</i>
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  Data: <searchLink fieldCode="JN" term="%22Evolutionary+Computation%22">Evolutionary Computation</searchLink>. Summer 2026, Vol. 34 Issue 2, p213-233. 21p.
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  Data: <searchLink fieldCode="DE" term="%22Subset+selection%22">Subset selection</searchLink><br /><searchLink fieldCode="DE" term="%22Heuristic%22">Heuristic</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+sampling%22">Statistical sampling</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Excellence%22">Excellence</searchLink>
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  Data: In real-world applications, users often favor structurally diverse design choices over one high-quality solution. It is therefore important to consider more solutions that decision makers can compare and further explore based on additional criteria. Alongside the existing approaches of evolutionary diversity optimization, quality diversity, and multimodal optimization, this paper presents a fresh perspective on this challenge by considering the problem of identifying a fixed number of solutions with a pairwise distance above a specified threshold while maximizing their average quality. We obtain first insight into these objectives by performing a subset selection on the search trajectories of different well-established search heuristics, whether they have been specifically designed with diversity in mind or not. We emphasize that the main goal of our work is not to present a new algorithm but to understand the capability of off-the-shelf algorithms to quantify the trade-off between the minimum pairwise distance within batches of solutions and their average quality. We also analyze how this trade-off depends on the properties of the underlying optimization problem. A possibly surprising outcome of our empirical study is the observation that naive uniform random sampling establishes a very strong baseline for our problem, hardly ever outperformed by the search trajectories of the considered heuristics. We interpret these results as a motivation to develop algorithms tailored to produce diverse solutions of high average quality. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Evolutionary Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1162/EVCO.a.28
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      – Code: eng
        Text: English
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        PageCount: 21
        StartPage: 213
    Subjects:
      – SubjectFull: Subset selection
        Type: general
      – SubjectFull: Heuristic
        Type: general
      – SubjectFull: Metaheuristic algorithms
        Type: general
      – SubjectFull: Statistical sampling
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Excellence
        Type: general
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      – TitleFull: Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization.
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            NameFull: Santoni, Maria Laura
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            NameFull: Raponi, Elena
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              M: 06
              Text: Summer 2026
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              Y: 2026
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