Maximum-Score Diversity Selection - Meinl, Thorsten
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This book discusses the problem of Maximum-Score Diversity Selection (MSDS). Pure diversity selection, as it is often performed e.g. in early drug discovery, is the selection of a subset of available objects that is as diverse as possible. MSDS adds a second objective, which additionally tries to maximize the "score" of the subset, which usually is the sum of scores of all elements in the subset. Thus, this problem is a classical multi-objective optimization problem since both objectives -- maximizing score and maximizing diversity -- tend to conflict with each other. The author first…mehr

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Produktbeschreibung
This book discusses the problem of Maximum-Score Diversity Selection (MSDS). Pure diversity selection, as it is often performed e.g. in early drug discovery, is the selection of a subset of available objects that is as diverse as possible. MSDS adds a second objective, which additionally tries to maximize the "score" of the subset, which usually is the sum of scores of all elements in the subset. Thus, this problem is a classical multi-objective optimization problem since both objectives -- maximizing score and maximizing diversity -- tend to conflict with each other. The author first discusses several diversity measures and then shows that MSDS is an NP-hard optimization problem. Subsequently several methods are presented and developed to efficiently solve this special multi-objective optimization problem. A large experimental evaluation compares the effectiveness of the presented algorithms and shows how they are influenced by the search space structure.
Autorenporträt
Thorsten Meinl studied Computer Science at theFriedrich-Alexander University Erlangen-Nuremberg and graduatedin 2004. In 2010 he received his PhD in engineering from theUniversity of Konstanz. He has more than 20 publications atinternational conferences and in journals.