A Population Diversity-Oriented Gene Expression Programming for Function Finding

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  title =        "A Population Diversity-Oriented Gene Expression
                 Programming for Function Finding",
  author =       "Ruochen Liu and Qifeng Lei and Jing Liu and 
                 Licheng Jiao",
  booktitle =    "8th International Conference on Simulated Evolution
                 and Learning (SEAL 2010)",
  year =         "2010",
  volume =       "6457",
  editor =       "Kalyanmoy Deb and Arnab Bhattacharya and 
                 Nirupam Chakraborti and Partha Chakroborty and Swagatam Das and 
                 Joydeep Dutta and Santosh K. Gupta and Ashu Jain and 
                 Varun Aggarwal and J{\"u}rgen Branke and 
                 Sushil J. Louis and Kay Chen Tan",
  series =       "Lecture Notes in Computer Science",
  pages =        "215--219",
  address =      "Kanpur, India",
  month =        dec # " 1-4",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  bibdate =      "2010-12-01",
  bibsource =    "DBLP,
  isbn13 =       "978-3-642-17297-7",
  DOI =          "doi:10.1007/978-3-642-17298-4",
  abstract =     "Gene expression programming (GEP) is a novel
                 evolutionary algorithm, which combines the advantages
                 of simple genetic algorithm (SGA) and genetic
                 programming (GP). Owing to its special structure of
                 linear encoding and nonlinear decoding, GEP has been
                 applied in various fields such as function finding and
                 data classification. In this paper, we propose a
                 modified GEP (Mod-GEP), in which, two strategies
                 including population updating and population pruning
                 are used to increase the diversity of population.
                 Mod-GEP is applied into two practical function finding
                 problems, the results show that Mod-GEP can get a more
                 satisfactory solution than that of GP, GEP and GEP
                 based on statistical analysis and stagnancy (AMACGEP",

Genetic Programming entries for Ruochen Liu Qifeng Lei Jing Liu Licheng Jiao