An Analysis and Evaluation of the Saving Capability and Feasibility of Backward-Chaining Evolutionary Algorithms

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@InProceedings{DBLP:conf/acal/XieZ09,
  author =       "Huayang Xie and Mengjie Zhang",
  title =        "An Analysis and Evaluation of the Saving Capability
                 and Feasibility of Backward-Chaining Evolutionary
                 Algorithms",
  booktitle =    "Proceedings of the 4th Australian Conference on
                 Artificial Life (ACAL'09)",
  series =       "Lecture Notes in Computer Science",
  volume =       "5865",
  year =         "2009",
  editor =       "Kevin B. Korb and Marcus Randall and Tim Hendtlass",
  pages =        "63--72",
  address =      "Melbourne, Australia",
  month =        dec # " 1-4",
  publisher =    "Springer",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-10426-8",
  DOI =          "doi:10.1007/978-3-642-10427-5_7",
  abstract =     "Artificial Intelligence, volume 170, number 11, pages
                 953-983, 2006 published a paper titled
                 {"}Backward-chaining evolutionary algorithm{"}
                 \cite{poli_2006_AIJ}. It introduced two fitness
                 evaluation saving algorithms which are built on top of
                 standard tournament selection. One algorithm is named
                 Efficient Macro-selection Evolutionary Algorithm
                 (EMS-EA) and the other is named Backward-chaining EA
                 (BC-EA). Both algorithms were claimed to be able to
                 provide considerable fitness evaluation savings, and
                 especially BC-EA was claimed to be much efficient for
                 hard and complex problems which require very large
                 populations. This paper provides an evaluation and
                 analysis of the two algorithms in terms of the
                 feasibility and capability of reducing the fitness
                 evaluation cost. The evaluation and analysis results
                 show that BC-EA would be able to provide computational
                 savings in unusual situations where given problems can
                 be solved by an evolutionary algorithm using a very
                 small tournament size, or a large tournament size but a
                 very large population and a very small number of
                 generations. Other than that, the saving capability of
                 BC-EA is the same as EMS-EA. Furthermore, the
                 feasibility of BC-EA is limited because two important
                 assumptions making it work hardly hold.",
}

Genetic Programming entries for Huayang Jason Xie Mengjie Zhang

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