Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics.
The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2015-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4546428?pdf=render |
| _version_ | 1818470201487785984 |
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| author | Vito Trianni Manuel López-Ibáñez |
| author_facet | Vito Trianni Manuel López-Ibáñez |
| author_sort | Vito Trianni |
| collection | DOAJ |
| description | The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled). However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i) allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii) supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii) avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv) solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics. |
| first_indexed | 2024-04-13T21:34:33Z |
| format | Article |
| id | doaj.art-b87b95c19e6d474c9e6bd374b7b0dbb7 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-13T21:34:33Z |
| publishDate | 2015-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-b87b95c19e6d474c9e6bd374b7b0dbb72022-12-22T02:29:02ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01108e013640610.1371/journal.pone.0136406Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics.Vito TrianniManuel López-IbáñezThe application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled). However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i) allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii) supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii) avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv) solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.http://europepmc.org/articles/PMC4546428?pdf=render |
| spellingShingle | Vito Trianni Manuel López-Ibáñez Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics. PLoS ONE |
| title | Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics. |
| title_full | Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics. |
| title_fullStr | Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics. |
| title_full_unstemmed | Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics. |
| title_short | Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics. |
| title_sort | advantages of task specific multi objective optimisation in evolutionary robotics |
| url | http://europepmc.org/articles/PMC4546428?pdf=render |
| work_keys_str_mv | AT vitotrianni advantagesoftaskspecificmultiobjectiveoptimisationinevolutionaryrobotics AT manuellopezibanez advantagesoftaskspecificmultiobjectiveoptimisationinevolutionaryrobotics |