- author = "Cezary Z. Janikow and Christopher J. Mann",
- title = "{CGP} visits the {Santa Fe} trail: effects of heuristics on {GP}",
- booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation",
- year = "2005",
- editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler",
- volume = "2",
- ISBN = "1-59593-010-8",
- pages = "1697--1704",
- address = "Washington DC, USA",
- publisher = "ACM Press",
- publisher_address = "New York, NY, 10286-1405, USA",
- month = "25-29 " # jun,
- organisation = "ACM SIGEVO (formerly ISGEC)",
- keywords = "genetic algorithms, genetic programming, Adaptable Constrained Genetic Programming, evolutionary computation, design, experimentation, heuristics",
-
URL = "
http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1697.pdf",
-
DOI = "
doi:10.1145/1068009.1068293",
-
code_url = "
http://www.cs.umsl.edu/~janikow/cgp-lilgp/",
- size = "8 pages",
- abstract = "GP uses trees to represent chromosomes. The user defines the representation space by defining the set of functions and terminals to label the nodes in the trees, and GP searches the space. Previous research and experimentation show that the choice of the function/terminal set, choice of the initial population, and some other explicit and implicit design factors have great influence on both the quality and the speed of the evolution. Such heuristics are valuable simply because they improve GP's performance, or because they enforce some desired properties on the solutions. In this paper, we evaluate the effect of heuristics on GP solving the Santa Fe trail. We concentrate on improving the solution quality, but we also look at efficiency. Various heuristics are tried and mixed by hand, while evaluated with the help of the CGP system. Results show that some heuristics result in very substantial performance improvements, that complex heuristics are usually not decomposable, and that the heuristics generalize to apply to other similar problems, but the applicability reduces with the complexity of the heuristics and the dissimilarity of the new problem to the old one. We also compare such user-mixed heuristics with those generated by the ACGP system which automatically extracts heuristics improving GP performance.",
-
notes = "http://www.cs.umsl.edu/~janikow/cgp-lilgp/
GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005).
ACM Order Number 910052",
