Grammar Model-based Program Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{shan:2004:gmpe,

• author = "Yin Shan and Robert I. McKay and Rohan Baxter and Hussein Abbass and Daryl Essam and Nguyen Xuan Hoai",
• title = "Grammar Model-based Program Evolution",
• booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation",
• pages = "478--485",
• year = "2004",
• publisher = "IEEE Press",
• month = "20-23 " # jun,
• address = "Portland, Oregon, USA",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming, Theory of evolutionary algorithms",
• URL = "http://sc.snu.ac.kr/courses/2006/fall/pg/aai/GP/shan/scfgcec04.pdf",
• DOI = "doi:10.1109/CEC.2004.1330895",
• size = "8 pages",
• abstract = "In Evolutionary Computation, fixed genetic operators may destroy the sub-solution, usually called building blocks, instead of discovering and preserving them. One way to overcome this problem is to build a model based on the good individuals, and sample this model to obtain the next population. In this paper, along this line, we propose a new method, Grammar Model-based Program Evolution (GMPE) to evolved GP program. We replace common GP genetic operator with a Probabilistic Context-free Grammar (SCFG). In each generation, an SCFG is learnt, and a new population is generated by sampling this SCFG model. On two benchmark problems we have studied, GMPE significantly outperforms conventional GP, learning faster and more reliably.",
• notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.",

}

Genetic Programming entries for Yin Shan R I (Bob) McKay Rohan Baxter Hussein A Abbass Daryl Essam Nguyen Xuan Hoai

Citations