Program evolution with explicit learning: a New Framework for Program Automatic Synthesis

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

@InProceedings{Shan:2003:Pewel,

• author = "Y. Shan and R. I. McKay and H. A. Abbass and D. Essam",
• title = "Program evolution with explicit learning: a New Framework for Program Automatic Synthesis",
• booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003",
• editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon",
• pages = "1639--1646",
• year = "2003",
• publisher = "IEEE Press",
• address = "Canberra",
• publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
• month = "8-12 " # dec,
• organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)",
• keywords = "genetic algorithms, genetic programming, Ant colony optimization, Australia, Computer science, Educational institutions, Probability distribution, Stochastic processes, Stochastic systems, learning (artificial intelligence), probability, software prototyping, GP genetic operators, estimation of distribution algorithms, evolutionary computing, evolutionary processing, probability distributions, program evolution with explicit learning",
• ISBN = "0-7803-7804-0",
• URL = "http://www.cs.adfa.edu.au/~shanyin/publications/peel.pdf",
• URL = "http://citeseer.ist.psu.edu/560804.html",
• DOI = "doi:10.1109/CEC.2003.1299869",
• size = "8 pages",
• abstract = "In genetic programming (GP) and most other evolutionary computing approaches, the knowledge learned during the evolutionary processing is implicitly encoded in the population. A small family of approaches, known as estimation of distribution algorithms, learn this knowledge directly in the form of probability distributions. In this research, we proposed a new approach for program synthesis - program evolution with explicit learning (PEEL), belonging to this family. PEEL learns probability distributions from previous generations and stochastically generates new populations according to this distribution. PEEL is intrinsically different from GP systems because it abandons conventional GP genetic operators and does not maintain population. On the benchmark problems we have studied, this approach shows at least comparable performance to GP.",
• notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.",

}

Genetic Programming entries for Yin Shan R I (Bob) McKay Hussein A Abbass Daryl Essam

Citations