Genetic Algorithms and Genetic Programming: Combining Strength in One Evolutionary Strategy

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Akbarzadeh:1997:jce,
  author =       "M.-R. Akbarzadeh-T. and E. Tunstel and M. Jamshidi",
  title =        "Genetic Algorithms and Genetic Programming: Combining
                 Strength in One Evolutionary Strategy",
  booktitle =    "Proceedings of the 1997 WERC/HSRC Joint Conference on
                 the Environment",
  year =         "1997",
  pages =        "373--377",
  address =      "Albuquerque, NM, USA",
  month =        "26-29 " # apr,
  organisation = "WERC Waste-management Education & Research Consortium
                 New Mexico State University Box 30001, Department WERC
                 Las Cruces, NM 88003-8001, USA

                 HSRC Great Plains/Rocky Mountain Hazardous Substance
                 Research Center Kansas State University 101 Ward Hall
                 Manhattan, KS 66506-2502, USA",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Akbarzadeh_1997_jce.pdf",
  size =         "5 pages",
  abstract =     "Genetic Algorithms (GA) and Genetic Programs (GP) are
                 two of the most widely used evolution strategies for
                 parameter optimisation of complex systems. GAs have
                 shown a great deal of success where the representation
                 space is a string of binary or real-valued numbers. At
                 the same time, GP has demonstrated success with
                 symbolic representation spaces and where structure
                 among symbols is explored. This paper discusses
                 weaknesses and strengths of GA and GP in search of a
                 combined and more evolved optimization algorithm. This
                 combination is especially attractive for problem
                 domains with non-homogeneous parameters. In particular,
                 a fuzzy logic membership function is represented by
                 numerical strings, whereas rule-sets are represented by
                 symbols and structural connectives. Two examples are
                 provided which exhibit how GA and GP are best used in
                 optimising robot performance in manipulating hazardous
                 waste. The first example involves optimisation for a
                 fuzzy controller for a flexible robot using GA and the
                 second example illustrates usage of GP in optimizing an
                 intelligent navigation algorithm for a mobile robot. A
                 novel strategy for combining GA and GP is presented.",
}

Genetic Programming entries for Mohammad-R Akbarzadeh-Totonchi Edward W Tunstel Mohammad Jamshidi

Citations