Artificial Evolution by Viability Rather than Competition
Evolutionary algorithms are widespread heuristic methods inspired by natural evolution to solve difficult problems for which analytical approaches are not suitable. In many domains experimenters are not only interested in discovering optimal solutions, but also in finding the largest number of diffe...
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| Format: | Online |
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Public Library of Science
2014
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3906060/ |
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pubmed-39060602014-01-31 Artificial Evolution by Viability Rather than Competition Maesani, Andrea Fernando, Pradeep Ruben Floreano, Dario Research Article Evolutionary algorithms are widespread heuristic methods inspired by natural evolution to solve difficult problems for which analytical approaches are not suitable. In many domains experimenters are not only interested in discovering optimal solutions, but also in finding the largest number of different solutions satisfying minimal requirements. However, the formulation of an effective performance measure describing these requirements, also known as fitness function, represents a major challenge. The difficulty of combining and weighting multiple problem objectives and constraints of possibly varying nature and scale into a single fitness function often leads to unsatisfactory solutions. Furthermore, selective reproduction of the fittest solutions, which is inspired by competition-based selection in nature, leads to loss of diversity within the evolving population and premature convergence of the algorithm, hindering the discovery of many different solutions. Public Library of Science 2014-01-29 /pmc/articles/PMC3906060/ /pubmed/24489790 http://dx.doi.org/10.1371/journal.pone.0086831 Text en © 2014 Maesani et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Maesani, Andrea Fernando, Pradeep Ruben Floreano, Dario |
| spellingShingle |
Maesani, Andrea Fernando, Pradeep Ruben Floreano, Dario Artificial Evolution by Viability Rather than Competition |
| author_facet |
Maesani, Andrea Fernando, Pradeep Ruben Floreano, Dario |
| author_sort |
Maesani, Andrea |
| title |
Artificial Evolution by Viability Rather than Competition |
| title_short |
Artificial Evolution by Viability Rather than Competition |
| title_full |
Artificial Evolution by Viability Rather than Competition |
| title_fullStr |
Artificial Evolution by Viability Rather than Competition |
| title_full_unstemmed |
Artificial Evolution by Viability Rather than Competition |
| title_sort |
artificial evolution by viability rather than competition |
| description |
Evolutionary algorithms are widespread heuristic methods inspired by natural evolution to solve difficult problems for which analytical approaches are not suitable. In many domains experimenters are not only interested in discovering optimal solutions, but also in finding the largest number of different solutions satisfying minimal requirements. However, the formulation of an effective performance measure describing these requirements, also known as fitness function, represents a major challenge. The difficulty of combining and weighting multiple problem objectives and constraints of possibly varying nature and scale into a single fitness function often leads to unsatisfactory solutions. Furthermore, selective reproduction of the fittest solutions, which is inspired by competition-based selection in nature, leads to loss of diversity within the evolving population and premature convergence of the algorithm, hindering the discovery of many different solutions. |
| publisher |
Public Library of Science |
| publishDate |
2014 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3906060/ |
| _version_ |
1612052570131398656 |