The Library
A discretization-free metric for assessing quality diversity algorithms
Tools
Tools
Tools
Kent, Paul, Branke, Juergen, Gaier, Adam and Mouret, Jean-Baptiste (2022) A discretization-free metric for assessing quality diversity algorithms. In: GECCO '22: Genetic and Evolutionary Computation Conference Companion, Massachusetts, Boston, 9-13 Jul 2022. Published in: GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion pp. 2131-2135. ISBN 9781450392686. doi:10.1145/3520304.3534018
Research output not available from this repository.
Request-a-Copy directly from author or use local Library Get it For Me service.
Official URL: https://doi.org/10.1145/3520304.3534018
Abstract
While Quality-Diversity algorithms attempt to produce a set of high quality solutions that are diverse throughout descriptor space, in reality decision makers are often interested in solutions with specific descriptor values. In this paper we suggest that current methods of evaluating Quality Diversity algorithm performance do not properly account for a decision maker's preference in a continuous descriptor space and suggest three approaches that attempt to capture the real-world trade-off between a solution's objective performance and distance from a desired set of target descriptors.
In this paper we propose a randomised metric, a process of Monte-Carlo sampling of n target points in descriptor space and a small number of random weights that represent different tolerances for mis-specification in a solution's descriptor values. This sampling allows us to simulate the requirements of all possible combinations of target-tolerance pairs and, by taking sufficient samples, estimate average performance.
We go on to formulate three simple methods for comparing average performance of algorithms; Continuous Quality Diversity score (CQD) and Hypervolume of the objective/distance Pareto front. We show that these measures are simple to implement and robust measures of performance without introducing artificial discretisation of the descriptor space.
| Item Type: | Conference Item (Paper) | ||||||
|---|---|---|---|---|---|---|---|
| Divisions: | Faculty of Social Sciences > Warwick Business School | ||||||
| SWORD Depositor: | Library Publications Router | ||||||
| Journal or Publication Title: | GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion | ||||||
| Publisher: | ACM | ||||||
| ISBN: | 9781450392686 | ||||||
| Official Date: | July 2022 | ||||||
| Dates: |
|
||||||
| Page Range: | pp. 2131-2135 | ||||||
| DOI: | 10.1145/3520304.3534018 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Conference Paper Type: | Paper | ||||||
| Title of Event: | GECCO '22: Genetic and Evolutionary Computation Conference Companion | ||||||
| Type of Event: | Conference | ||||||
| Location of Event: | Massachusetts, Boston | ||||||
| Date(s) of Event: | 9-13 Jul 2022 |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
