Multi-dimensional Path Planning Evolutionary Computation using Evolutionary Computation

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

@InProceedings{hocaoglu:1998:,
  author =       "Cem Hocaoglu and Arthur C. Sanderson",
  title =        "Multi-dimensional Path Planning Evolutionary
                 Computation using Evolutionary Computation",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "165--170",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, amplifiers,
                 analog circuit design, circuit evolution, computational
                 circuits, embryonic circuit elimination, filters,
                 knowledge representation, minimal domain knowledge,
                 problem-specific knowledge, analogue circuits, circuit
                 CAD, circuit optimisation, intelligent design
                 assistants, knowledge representation, programming",
  ISBN =         "0-7803-4869-9",
  file =         "c029.pdf",
  DOI =          "doi:10.1109/ICEC.1998.699495",
  size =         "6 pages",
  abstract =     "This paper describes a flexible and efficient
                 multi-dimensional path planning algorithm based on
                 evolutionary computation concepts. A novel iterative
                 multi-resolution path representation is used as a basis
                 for the GA coding. The use of a multi-resolution path
                 representation can reduce the expected search length
                 for the path planning problem. If a successful path is
                 found early in the search hierarchy (at a low level of
                 resolution), then further expansion of that portion of
                 the path search is not necessary. This advantage is
                 mapped into the encoded search space and adjusts the
                 string length accordingly. The algorithm is flexible;
                 it handles multi-dimensional path planning problems,
                 accommodates different optimization criteria and
                 changes in these criteria, and it uses domain specific
                 knowledge for making decisions. In the evolutionary
                 path planner, the individual candidates are evaluated
                 with respect to the workspace so that computation of
                 the configuration space is not required. The algorithm
                 can be applied for planning paths for mobile robots,
                 assembly, pianomovers problems and articulated
                 manipulators. The effectiveness of the algorithm is
                 demonstrated on a number of multi-dimensional path
                 planning problems.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence",
}

Genetic Programming entries for Cem Hocaoglu Arthur C Sanderson

Citations