Improving the size and quality of MAP-Elites containers via multiple emitters and decoders for urban logistics

Urquhart, Neil; Hart, Emma

Abstract

Quality-diversity (QD) methods such as MAP-Elites have been demonstrated to be useful in the domain of combinatorial optimisation due to their ability to generate a large set of solutions to a single-objective problem that are diverse with respect to user-defined features of interest. However, filling a MAP-Elites container with solutions can require careful design of operators to ensure complete exploration of the feature-space. Working in the domain of urban logistics, we propose two methods to increase exploration. Firstly, we exploit multiple decodings of the same genome which can generate different offspring from the same parent solution. Secondly, we make use of a multiple mutation operators to generate offspring from a parent, using a multi-armed bandit algorithm to adaptively select the best operator during the search. Our results on a set of 48 instances show that both the number of solutions within the container and the qd score of the container (indicating quality) can be significantly increased compared to the standard MAP-Elites approach.

Citation

Urquhart, N., & Hart, E. (in press). Improving the size and quality of MAP-Elites containers via multiple emitters and decoders for urban logistics. In Applications of Evolutionary Computation – 26th International Conference, EvoApplications 2023

• Conference Name: Evo Applications 2023
• Conference Location: Brno, Czech Republic
• Start Date: Apr 12, 2023
• End Date: Apr 14, 2023
• Acceptance Date: Jan 18, 2023
• Deposit Date: Jan 27, 2023
• Publisher: Springer
• Series Title: Lecture Notes in Computer Science
• Series Number: 13989
• Series ISSN: 0302-9743
• Book Title: Applications of Evolutionary Computation – 26th International Conference, EvoApplications 2023
• ISBN: 9783031302282
• Related Public URLs: https://link.springer.com/book/9783031302282