Refining Fitness Functions and Optimising Training Data in GP for Object Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Mengjie Zhang and Malcolm Lett and Yuejin Ma",
  title =        "Refining Fitness Functions and Optimising Training
                 Data in GP for Object Detection",
  booktitle =    "Simulated Evolution and Learning, Proceedings 6th
                 International Conference, SEAL 2006",
  year =         "2006",
  pages =        "601--608",
  DOI =          "doi:10.1007/11903697_76",
  bibsource =    "DBLP,",
  editor =       "Tzai-Der Wang and Xiaodong Li and Shu-Heng Chen and 
                 Xufa Wang and Hussein A. Abbass and Hitoshi Iba and 
                 Guoliang Chen and Xin Yao",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "4247",
  ISBN =         "3-540-47331-9",
  address =      "Hefei, China",
  month =        oct # " 15-18",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "This paper describes an approach to the refinement of
                 a fitness function and the optimisation of training
                 data in genetic programming for object detection
                 particularly object localisation problems. The approach
                 is examined and compared with an existing fitness
                 function on three object detection problems of
                 increasing difficulty. The results suggest that the new
                 fitness function outperforms the old one by producing
                 far fewer false alarms and spending much less training
                 time and that some particular types of training
                 examples contain most of the useful information for
                 object detection.",

Genetic Programming entries for Mengjie Zhang Malcolm Lett Yuejin Ma