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These algorithms and controllers are well suited to autonomously find solutions for decentralized collective robotic tasks based on principles of self-organization. The article first presents a taxonomy of role-taking and specialization mechanisms related to evolved neural network controllers. Then it introduces two cooperation tasks, which can be accomplished by either role taking or specialization, and uses these tasks to compare four different genetic algorithms to evaluate their capacity to evolve a suitable behavioral strategy, which depends on the task demands. Interestingly, only one of the four algorithms, which appears to have more biological plausibility, is capable of evolving role taking or specialization when they are needed. The results are relevant for both collective robotics and biology, as they can provide useful hints on the different processes that can lead to the emergence of specialization in robots and organisms.<\/jats:p>","DOI":"10.1162\/artl_a_00106","type":"journal-article","created":{"date-parts":[[2013,3,20]],"date-time":"2013-03-20T18:23:40Z","timestamp":1363803820000},"page":"221-253","source":"Crossref","is-referenced-by-count":5,"title":["Different Genetic Algorithms and the Evolution of Specialization: A Study with Groups of Simulated Neural Robots"],"prefix":"10.1162","volume":"19","author":[{"given":"Tomassino","family":"Ferrauto","sequence":"first","affiliation":[{"name":"Consiglio Nazionale delle Ricerche"},{"name":"Universit\u00e0 degli Studi dell'Aquila"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Domenico","family":"Parisi","sequence":"additional","affiliation":[{"name":"Consiglio Nazionale delle 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