Coevolutionary Pareto diversity optimization
Date
2022
Authors
Neumann, A.
Antipov, D.
Neumann, F.
Editors
Fieldsend, J.E.
Wagner, M.
Wagner, M.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'22), 2022 / Fieldsend, J.E., Wagner, M. (ed./s), vol.abs/2204.05457, pp.832-839
Statement of Responsibility
Aneta Neumann, Denis Antipov, Frank Neumann
Conference Name
Genetic and Evolutionary Computation Conference (GECCO) (9 Jul 2022 - 13 Jul 2022 : virtual online)
Abstract
Computing diverse sets of high quality solutions for a given optimization problem has become an important topic in recent years. In this paper, we introduce a coevolutionary Pareto Diversity Optimization approach which builds on the success of reformulating a constrained single-objective optimization problem as a bi-objective problem by turning the constraint into an additional objective. Our new Pareto Diversity optimization approach uses this bi-objective formulation to optimize the problem while also maintaining an additional population of high quality solutions for which diversity is optimized with respect to a given diversity measure. We show that our standard co-evolutionary Pareto Diversity Optimization approach outperforms the recently introduced DIVEA algorithm which obtains its initial population by generalized diversifying greedy sampling and improving the diversity of the set of solutions afterwards. Furthermore, we study possible improvements of the Pareto Diversity Optimization approach. In particular, we show that the use of inter-population crossover further improves the diversity of the set of solutions.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
© 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.