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Indicator-Based Versus Aspect-Based Selection in Multi- and Many-Objective Biochemical Optimization

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Bioinspired Optimization Methods and Their Applications (BIOMA 2018)

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Abstract

The identification of qualified peptides as ligands for diagnostic and therapeutic interventions requires the solution of multi- and many-objective biochemical optimization problems. A MOEA has been designed for molecular optimization with a combined indicator- and Pareto-based selection strategy that encounters common classification problems of the solutions’ quality with the rise of the problem dimension. Therefore, a sophisticated selection strategy is presented in this work that selects the individuals for the succeeding generation related to two general aspects in biochemical optimization: the first aspect reflects the peptide quality and the second one the genetic dissimilarity among the peptides in a population. The search behavior of this aspect-based selection is compared to the traditional selection on generic 3- to 6-dimensional physiochemical optimization problems and the impact of the reference point in the aspect-based selection is investigated.

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Author information

Authors and Affiliations

  1. Steinbeis Innovation Center “Intelligent and Self-Optimizing Software Assistance Systems”, Paracelsusstr. 9, Bergisch Gladbach, Germany

    Susanne Rosenthal & Markus Borschbach

  2. University of Applied Sciences (FHDW), Hauptstr. 2, Bergisch Gladbach, Germany

    Markus Borschbach

Authors
  1. Susanne Rosenthal
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  2. Markus Borschbach
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Corresponding authors

Correspondence to Susanne Rosenthal or Markus Borschbach .

Editor information

Editors and Affiliations

  1. Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia

    Peter Korošec

  2. University of Lille, Lille, France

    Nouredine Melab

  3. University of Lille, Lille, France

    El-Ghazali Talbi

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Rosenthal, S., Borschbach, M. (2018). Indicator-Based Versus Aspect-Based Selection in Multi- and Many-Objective Biochemical Optimization. In: Korošec, P., Melab, N., Talbi, EG. (eds) Bioinspired Optimization Methods and Their Applications. BIOMA 2018. Lecture Notes in Computer Science(), vol 10835. Springer, Cham. https://doi.org/10.1007/978-3-319-91641-5_22

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  • DOI: https://doi.org/10.1007/978-3-319-91641-5_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91640-8

  • Online ISBN: 978-3-319-91641-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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