Genetic Programming: Issues and Applications

Evolutionary Programming VI

Volume 1213 of the series Lecture Notes in Computer Science pp 85-100


Performance enhanced genetic programming

  • Chris ClackAffiliated withDepartment of Computer Science, University College London
  • , Tina YuAffiliated withDepartment of Computer Science, University College London

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Genetic Programming is increasing in popularity as the basis for a wide range of learning algorithms. However, the technique has to date only been successfully applied to modest tasks because of the performance overheads of evolving a large number of data structures, many of which do not correspond to a valid program. We address this problem directly and demonstrate how the evolutionary process can be achieved with much greater efficiency through the use of a formally-based representation and strong typing. We report initial experimental results which demonstrate that our technique exhibits significantly better performance than previous work.