Efficient Construction of Image Feature Extraction Programs by Using Linear Genetic Programming with Fitness Retrieval and Intermediate-Result Caching

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

@InCollection{series/sci/WatchareeruetaiMTKO09,

• author = "Ukrit Watchareeruetai and Tetsuya Matsumoto and Yoshinori Takeuchi and Hiroaki Kudo and Noboru Ohnishi",
• title = "Efficient Construction of Image Feature Extraction Programs by Using Linear Genetic Programming with Fitness Retrieval and Intermediate-Result Caching",
• booktitle = "Foundations of Computational Intelligence - Volume 4: Bio-Inspired Data Mining",
• publisher = "Springer",
• year = "2009",
• volume = "204",
• editor = "Ajith Abraham and Aboul Ella Hassanien and Andr{\'e} Carlos Ponce Leon Ferreira {de Carvalho}",
• series = "Studies in Computational Intelligence",
• pages = "355--375",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-642-01087-3",
• URL = "http://dx.doi.org/10.1007/978-3-642-01088-0",
• DOI = "doi:10.1007/978-3-642-01088-0_15",
• abstract = "This chapter describes a bio-inspired approach for automatic construction of feature extraction programs (FEPs) for a given object recognition problem. The goal of the automatic construction of FEPs is to cope with the difficulties in FEP design. Linear genetic programming (LGP) [4], a variation of evolutionary algorithms, is adopted. A population of FEPs is constructed from a set of basic image processing operations-which are used as primitive operators (POs), and their performances are optimised in the evolutionary process. Here we describe two techniques that improve the efficiency of the LGP-based program construction. One is to use fitness retrieval to avoid wasteful evaluations of the programs discovered before. The other one is to use intermediate-result caching, to avoid evaluation of the program-parts which were recently executed. The experimental results show that much computation time of the LGP-based FEP construction can be reduced by using these two techniques.",
• bibdate = "2010-04-20",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/series/sci/sci204.html#WatchareeruetaiMTKO09",

}

Genetic Programming entries for Ukrit WatchAreeruetai Tetsuya Matsumoto Yoshinori Takeuchi Hiroaki Kudo Noboru Ohnishi

Citations