Is Genetic Programming ``Human-Competitive''? The Case of Experimental Double Auction Markets

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

@InProceedings{conf/ideal/ChenS11,
  author =       "Shu-Heng Chen and Kuo-Chuan Shik",
  title =        "Is Genetic Programming ``Human-Competitive''? The Case
                 of Experimental Double Auction Markets",
  booktitle =    "Proceedings of the 12th International Conference on
                 Intelligent Data Engineering and Automated Learning,
                 {IDEAL} 2011",
  year =         "2011",
  editor =       "Hujun Yin and Wenjia Wang and 
                 Victor J. Rayward-Smith",
  volume =       "6936",
  series =       "Lecture Notes in Computer Science",
  pages =        "116--126",
  address =      "Norwich, UK",
  month =        sep # " 7-9",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, experimental
                 markets, double auctions, working memory capacity",
  isbn13 =       "978-3-642-23877-2",
  DOI =          "doi:10.1007/978-3-642-23878-9_15",
  size =         "11 pages",
  abstract =     "In this paper, the performance of human subjects is
                 compared with genetic programming in trading. Within a
                 kind of double auction market, we compare the learning
                 performance between human subjects and autonomous
                 agents whose trading behaviour is driven by genetic
                 programming (GP). To this end, a learning index based
                 upon the optimal solution to a double auction market
                 problem, characterised as integer programming, is
                 developed, and criteria tailor-made for humans are
                 proposed to evaluate the performance of both human
                 subjects and software agents. It is found that GP
                 robots generally fail to discover the best strategy,
                 which is a two-stage procrastination strategy, but some
                 human subjects are able to do so. An analysis from the
                 point of view of cognitive psychology further shows
                 that the minority who were able to find this best
                 strategy tend to have higher working memory capacities
                 than the majority who failed to do so. Therefore, even
                 though GP can outperform most human subjects, it is not
                 human-competitive from a higher standard.",
  affiliation =  "AIECON research center, Department of Economics,
                 National Chengchi University, Taipei, Taiwan",
  bibdate =      "2011-08-25",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/ideal/ideal2011.html#ChenS11",
}

Genetic Programming entries for Shu-Heng Chen Kuo-Chuan Shik

Citations