On using Gene Expression Programming to evolve multiple output robot controllers

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  title =        "On using Gene Expression Programming to evolve
                 multiple output robot controllers",
  author =       "Jonathan Mwaura and Ed Keedwell",
  publisher =    "IEEE",
  year =         "2014",
  pages =        "173--180",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  bibdate =      "2015-01-20",
  bibsource =    "DBLP,
  booktitle =    "ICES",
  isbn13 =       "978-1-4799-4480-4",
  URL =          "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7000191",
  DOI =          "doi:10.1109/ICES.2014.7008737",
  abstract =     "Most evolutionary algorithms (EAs) represents a
                 potential solution to a problem as a single-gene
                 chromosome encoding, where the chromosome gives only
                 one output to the problem. However, where more than one
                 output to a problem is required such as in
                 classification and robotic problems, these EAs have to
                 be either modified in order to deal with a multiple
                 output problem or are rendered incapable of dealing
                 with such problems. This paper investigates the
                 parallelisation of genes as independent chromosome
                 entities as described in the Gene Expression
                 Programming (GEP) algorithm. The aim is to investigate
                 the capabilities of a multiple output GEP (moGEP)
                 technique and compare its performance to that of a
                 single-gene GEP chromosome (ugGEP). In the described
                 work, the two GEP approaches are used to evolve
                 controllers for a robotic obstacle avoidance and
                 exploration behaviour. The obtained results shows that
                 moGEP is a robust technique for the investigated
                 problem class as well as for use in evolutionary

Genetic Programming entries for Jonathan Mwaura Ed Keedwell