{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,3]],"date-time":"2022-04-03T18:56:37Z","timestamp":1649012197731},"reference-count":7,"publisher":"World Scientific Pub Co Pte Lt","issue":"01n02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[1999,2]]},"abstract":"<jats:p> This paper proposes a method to improve robustness of the robot programs generated by genetic programming. The main idea is to inject perturbation into the simulation during the evolution of the solutions. The resulting robot programs are more robust because they have evolved to tolerate the changes in their environment. We set out to test this idea using the problem of navigating a mobile robot from a starting point to a target in an unknown cluttered environment. The result of the experiments shows the effectiveness of this scheme. The analysis of the result shows that the robustness depends on the \"experience\" that a robot program acquired during evolution. To improve robustness, the size of the set of \"experience\" should be increased and\/or the amount of reusing the \"experience\" should be increased. <\/jats:p>","DOI":"10.1142\/s0218126699000128","type":"journal-article","created":{"date-parts":[[2003,1,22]],"date-time":"2003-01-22T12:24:14Z","timestamp":1043238254000},"page":"133-143","source":"Crossref","is-referenced-by-count":1,"title":["USING PERTURBATION TO IMPROVE ROBUSTNESS OF SOLUTIONS GENERATED BY GENETIC  PROGRAMMING FOR ROBOT LEARNING"],"prefix":"10.1142","volume":"09","author":[{"given":"PRABHAS","family":"CHONGSTITVATANA","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Chulalongkorn University, Phayathai Rd., Bangkok 10330, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"p_8","first-page":"209","volume":"19","author":"Dorigo M.","year":"1995","journal-title":"Machine Learning"},{"key":"p_13","first-page":"301","author":"Prateeptongkum M.","year":"1999","journal-title":"Bangkok"},{"key":"p_14","doi-asserted-by":"publisher","DOI":"10.1016\/S0921-8890(96)00034-6"},{"key":"p_15","first-page":"675","author":"Olmer M.","year":"1996","journal-title":"New York"},{"key":"p_17","first-page":"2","author":"Miglino O.","year":"1996","journal-title":"Artificial Life"},{"key":"p_18","first-page":"501","author":"Lee W.","year":"1997","journal-title":"Proc. IEEE Int. Conf. Evol. Comp."},{"key":"p_19","doi-asserted-by":"publisher","DOI":"10.1177\/105971239700500201"}],"container-title":["Journal of Circuits, Systems and Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218126699000128","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T03:45:05Z","timestamp":1565149505000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218126699000128"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1999,2]]},"references-count":7,"journal-issue":{"issue":"01n02","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[1999,2]]}},"alternative-id":["10.1142\/S0218126699000128"],"URL":"https:\/\/doi.org\/10.1142\/s0218126699000128","relation":{},"ISSN":["0218-1266","1793-6454"],"issn-type":[{"value":"0218-1266","type":"print"},{"value":"1793-6454","type":"electronic"}],"subject":[],"published":{"date-parts":[[1999,2]]}}}