- author = "Brian W. Goldman and William F. Punch",
- title = "Length bias and search limitations in cartesian genetic programming",
- booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference",
- year = "2013",
- editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi",
- isbn13 = "978-1-4503-1963-8",
- pages = "933--940",
- keywords = "genetic algorithms, genetic programming, cartesian genetic programming",
- month = "6-10 " # jul,
- organisation = "SIGEVO",
- address = "Amsterdam, The Netherlands",
-
DOI = "
doi:10.1145/2463372.2463482",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
-
abstract = "In this paper we examine how Cartesian Genetic
Programming's (CGP's) method for encoding directed
acyclic graphs (DAGs) and its mutation operator bias
the effective length of individuals as well as the
distribution of inactive nodes in the genome. We
investigate these biases experimentally using two CGP
variants as comparisons: Reorder, a method for
shuffling node ordering without effecting individual
evaluation, and DAG, a method for removing the concept
of node position. Experiments were performed on four
problems tailored to highlight potential search
limitations, with further testing on the 3-bit
multiplier problem.
Unlike previous work, our experiments show that CGP has an innate parsimony pressure that makes it very difficult to evolve individuals with a high percentage of active nodes. This bias is particularly prevalent as the length of an individual increases. Furthermore, these problems are compounded by CGP's positional biases which can make some problems effectively unsolvable. Both Reorder and DAG appear to avoid these problems and outperform Normal CGP on preliminary benchmark testing. Finally, these new techniques require more reasonable genome sizes than those suggested in current CGP, with some evidence that solutions are also more terse.",
- notes = "Also known as \cite{2463482} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)",
