Improving Performance of GP by Adaptive Terminal Selection

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{ok00improving,

• author = "Sooyol Ok and Kazuo Miyashita and Seiichi Nishihara",
• title = "Improving Performance of {GP} by Adaptive Terminal Selection",
• booktitle = "PRICAI 2000 Topics in Artificial Intelligence: 6th Pacific Rim International Conference on Artificial Intelligence",
• pages = "435--445",
• year = "2000",
• editor = "Riichiro Mizoguchi and John K. Slaney",
• series = "Lecture Notes in Artifical Intelligence",
• volume = "1886",
• address = "Melbourne Convention Centre, Austrlia",
• month = "28 " # aug # "-1 " # sep,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-67925-1",
• bibsource = "DBLP, http://dblp.uni-trier.de",
• URL = "http://staff.aist.go.jp/k.miyashita/publications/PRICAI2000.ps",
• URL = "http://citeseer.ist.psu.edu/394599.html",
• abstract = "Genetic Programming (GP) is an evolutionary search algorithm which searches a computer program capable of producing the desired solution for a given problem. For the purpose, it is necessary that GP system has access to a set of features that are at least a superset of the features necessary to solve the problem. However, when the feature set given to GP is redundant, GP su ers substantial loss of its eciency. This paper presents a new approach in GP to acquire relevant terminals from a redundant set of terminals. We propose the adaptive mutation based on terminal weighting mechanism for eliminating irrelevant terminals from the redundant terminal set. We show empirically that the proposed method is effective for finding relevant terminals and improving performance of GP in the experiments on symbolic regression problems.",
• notes = "PRICAI 2000 http://www3.cm.deakin.edu.au/pricai/ broken Mar 2021",

}

Genetic Programming entries for Sooyol Ok Kazuo Miyashita Seiichi Nishihara

Citations