{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T21:17:11Z","timestamp":1778707031314,"version":"3.51.4"},"reference-count":45,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[1992,6,1]],"date-time":"1992-06-01T00:00:00Z","timestamp":707356800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Adaptive Behavior"],"published-print":{"date-parts":[[1992,6]]},"abstract":"<jats:p>We would like the behavior of the artificial agents that we construct to be as well-adapted to their environments as natural animals are to theirs. Unfortunately, designing controllers with these properties is a very difficult task. In this article, we demonstrate that continuous-time recurrent neural networks are a viable mechanism for adaptive agent control and that the genetic algorithm can be used to evolve effective neural controllers. A significant advantage of this approach is that one need specify only a measure of an agent's overall performance rather than the precise motor output trajectories by which it is achieved. By manipulating the performance evaluation, one can place selective pressure on the development of controllers with desired properties. Several novel controllers have been evolved, including a chemotaxis controller that switches between different strategies depending on environmental conditions, and a locomotion controller that takes advantage of sensory feedback if available but that can operate in its absence if necessary.<\/jats:p>","DOI":"10.1177\/105971239200100105","type":"journal-article","created":{"date-parts":[[2007,3,11]],"date-time":"2007-03-11T01:45:25Z","timestamp":1173577525000},"page":"91-122","source":"Crossref","is-referenced-by-count":361,"title":["Evolving Dynamical Neural Networks for Adaptive Behavior"],"prefix":"10.1177","volume":"1","author":[{"given":"Randall D.","family":"Beer","sequence":"first","affiliation":[{"name":"Department of Computer Engineering and Science Department of Biology Case Western Reserve University Cleveland, OH 44106"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John C.","family":"Gallagher","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Science Case Western Reserve University Cleveland, OH 44106"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[1992,6,1]]},"reference":[{"key":"atypb1","volume-title":"Proceedings of the Sixth National Conference on Artificial Intelligence","author":"Agre, P."},{"key":"atypb2","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/4943.001.0001"},{"key":"atypb3","volume-title":"Proceedings of the Second International Conference on Genetic Algorithms","author":"Baker, J.E."},{"key":"atypb4","volume-title":"Intelligence as adaptive behavior: An experiment in computational neuroethology","author":"Beer, R.D.","year":"1990"},{"key":"atypb5","volume-title":"Heterogeneous neural networks for adaptive behavior in dynamic environments","author":"Beer, R.D.","year":"1989"},{"key":"atypb6","volume-title":"Evolving networks: Using the genetic algorithm with connectionist learning","author":"Belew, R.K.","year":"1991"},{"key":"atypb7","doi-asserted-by":"publisher","DOI":"10.1007\/BF00113896"},{"key":"atypb8","doi-asserted-by":"publisher","DOI":"10.1109\/JRA.1986.1087032"},{"key":"atypb9","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.2.253"},{"key":"atypb10","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(91)90053-M"},{"key":"atypb11","volume-title":"Proceedings of the Fifth International Conference on Machine Learning","author":"Caruana, R."},{"key":"atypb12","volume-title":"In Proceedings of the International Joint Conference on Neural Networks","author":"Chiel, H.J."},{"key":"atypb13","volume-title":"From animals to animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior","author":"Collins, R.J."},{"key":"atypb14","volume-title":"Proceedings of the Seventh International Conference on Machine Learning","author":"de Garis, H."},{"key":"atypb15","doi-asserted-by":"publisher","DOI":"10.1126\/science.7423199"},{"key":"atypb16","doi-asserted-by":"publisher","DOI":"10.1007\/BF00203032"},{"key":"atypb17","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-5858-0_1"},{"key":"atypb18","volume-title":"Genetic algorithms in search, optimization, and machine learning","author":"Goldberg, D.E.","year":"1989"},{"key":"atypb19","doi-asserted-by":"publisher","DOI":"10.1016\/S0065-2806(08)60039-9"},{"key":"atypb20","volume-title":"Proceedings of the Third International Conference on Genetic Algorithms","author":"Harp, S.A."},{"key":"atypb21","volume-title":"Adaptation in natural and artificial systems","author":"Holland, J.","year":"1975"},{"key":"atypb22","volume-title":"Proceedings of the National Academy of Sciences of the United States of America","author":"Hopfield, J.J."},{"key":"atypb23","doi-asserted-by":"publisher","DOI":"10.1016\/S0921-8890(05)80027-2"},{"key":"atypb24","volume-title":"Artificial Life II","author":"Langton, C. 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