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Bayesian quality diversity search with interactive illumination
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Kent, Paul and Branke, Juergen (2023) Bayesian quality diversity search with interactive illumination. In: The Genetic and Evolutionary Computation Conference (GECCO), 15-19 Jul 2023, Lisbon, Portugal ; Hybrid. Published in: GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference pp. 1019-1026. ISBN 9798400701191. doi:10.1145/3583131.3590486
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Official URL: https://doi.org/10.1145/3583131.3590486
Abstract
This paper presents a novel way for interactively identifying a most preferable solution based on quality and behavioural characteristics. Our algorithm combines the principles of Quality-Diversity Search and Bayesian Optimization to create Gaussian Process surrogate models of the behaviour and fitness space. Unlike traditional Quality-Diversity methods which aim to find good solutions with different behavioural characteristics, we propose a three-step interactive approach that allows a decision maker to effciently identify the most preferred solution(s). In the first stage, it uses an entropybased acquisition function to generate an illumination model, followed by an interactive phase where the decision maker can specify regions of interest and a target behaviour. These preferences are then utilized by an improvement greedy acquisition function to guide the optimization process and quickly identify a solution close to the user-specified target. In a case study, with a simulated decision maker, we demonstrate that our approach can find better solutions much more quickly than by selecting the most preferred solution from an archive generated with MAP-Elites.
| Item Type: | Conference Item (Paper) | |||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | |||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Mathematics Faculty of Social Sciences > Warwick Business School > Operational Research & Management Sciences Faculty of Social Sciences > Warwick Business School |
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| Library of Congress Subject Headings (LCSH): | Evolutionary computation, Bayesian statistical decision theory, Gaussian processes, Machine learning, Artificial intelligence, Lighting -- Computer simulation -- Mathematical models, Computer graphics | |||||||||
| Journal or Publication Title: | GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference | |||||||||
| Publisher: | ACM | |||||||||
| ISBN: | 9798400701191 | |||||||||
| Official Date: | July 2023 | |||||||||
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| Page Range: | pp. 1019-1026 | |||||||||
| DOI: | 10.1145/3583131.3590486 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||
| Date of first compliant deposit: | 19 April 2023 | |||||||||
| Date of first compliant Open Access: | 19 July 2023 | |||||||||
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| Conference Paper Type: | Paper | |||||||||
| Title of Event: | The Genetic and Evolutionary Computation Conference (GECCO) | |||||||||
| Type of Event: | Conference | |||||||||
| Location of Event: | 15-19 Jul 2023 | |||||||||
| Date(s) of Event: | Lisbon, Portugal ; Hybrid | |||||||||
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