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DC Field | Value | Language |
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dc.contributor.author | Gallotta, Roberto | - |
dc.contributor.author | Liapis, Antonios | - |
dc.contributor.author | Yannakakis, Georgios N. | - |
dc.date.accessioned | 2024-04-30T10:09:02Z | - |
dc.date.available | 2024-04-30T10:09:02Z | - |
dc.date.issued | 2024 | - |
dc.identifier.citation | Gallotta, R., Liapis, A., & Yannakakis, G. N. (2024). Dynamic quality-diversity search. Genetic and Evolutionary Computation Conference Companion. Melbourne, Australia. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/121627 | - |
dc.description.abstract | Evolutionary search via the quality-diversity (QD) paradigm can discover highly performing solutions in different behavioural niches, showing considerable potential in complex real-world scenarios such as evolutionary robotics. Yet most QD methods only tackle static tasks that are fixed over time, which is rarely the case in the real world. Unlike noisy environments, where the fitness of an individual changes slightly at every evaluation, dynamic environments simulate tasks where external factors at unknown and irregular intervals alter the performance of the individual with a severity that is unknown a priori. Literature on optimisation in dynamic environments is extensive, yet such environments have not been explored in the context of QD search. This paper introduces a novel and generalisable Dynamic QD methodology that aims to keep the archive of past solutions updated in the case of environment changes. Our Dynamic QD intervention is applied on MAP-Elites and CMA-ME, two powerful QD algorithms, and we test their performance on a dynamic variant of the well-known lunar lander environment. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Association for Computing Machinery | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Computer games -- Design | en_GB |
dc.subject | Genetic algorithms | en_GB |
dc.subject | Evolutionary computation | en_GB |
dc.subject | Artificial intelligence | en_GB |
dc.subject | Algorithms | en_GB |
dc.subject | Human-computer interaction | en_GB |
dc.title | Dynamic quality-diversity search | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.bibliographicCitation.conferencename | Genetic and Evolutionary Computation Conference Companion | en_GB |
dc.bibliographicCitation.conferenceplace | Melbourne, Australia. 14-18/07/2024 | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1145/3638530.3654257 | - |
Appears in Collections: | Scholarly Works - InsDG |
Files in This Item:
File | Description | Size | Format | |
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dynamic_quality-diversity_search.pdf | 462.18 kB | Adobe PDF | View/Open |
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