Using Perturbation To Improve Robustness Of Solutions Generated By Genetic Programming For Robot Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@Article{oai:CiteSeerPSU:421006,

• title = "Using Perturbation To Improve Robustness Of Solutions Generated By Genetic Programming For Robot Learning",
• author = "Prabhas Chongstitvatana",
• journal = "Journal of Circuits, Systems and Computers",
• year = "1999",
• volume = "9",
• number = "1-2",
• pages = "133--143",
• publisher = "World Scientific Publishing Company",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://www.worldscinet.com/123/09/0901n02/S0218126699000128.html",
• DOI = "doi:10.1142/S0218126699000128",
• citeseer-isreferencedby = "oai:CiteSeerPSU:397249; oai:CiteSeerPSU:59033",
• citeseer-references = "oai:CiteSeerPSU:212034; oai:CiteSeerPSU:51923; oai:CiteSeerPSU:70404; oai:CiteSeerPSU:23925; oai:CiteSeerPSU:61708; oai:CiteSeerPSU:14506; oai:CiteSeerPSU:160348; oai:CiteSeerPSU:115106",
• annote = "The Pennsylvania State University CiteSeer Archives",
• language = "en",
• oai = "oai:CiteSeerPSU:421006",
• rights = "unrestricted",
• URL = "http://citeseer.ist.psu.edu/421006.html",
• abstract = "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.",
• notes = "discrete 2D 500x750 simulation, smellLeft,smellRight",

}

Genetic Programming entries for Prabhas Chongstitvatana

Citations