- author = "Leonardo Trujillo and Yuliana Martinez and Edgar Galvan Lopez and Pierrick Legrand",
- title = "A comparative study of an evolvability indicator and a predictor of expected performance for genetic programming",
- booktitle = "GECCO Companion '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion",
- year = "2012",
- editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb",
- isbn13 = "978-1-4503-1178-6",
- keywords = "genetic algorithms, Genetic programming: Poster",
- pages = "1489--1490",
- month = "7-11 " # jul,
- organisation = "SIGEVO",
- address = "Philadelphia, Pennsylvania, USA",
-
DOI = "
doi:10.1145/2330784.2331006",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- abstract = "An open question within Genetic Programming (GP) is how to characterize problemdifficulty. The goal is to develop predictive tools that estimate how difficult a problemis for GP to solve. Here we consider two groups of methods. We call the first group Evolvability Indicators (EI), measures that capture how amendable the fitness landscape is to a GP search. Examples of EIs are Fitness Distance Correlation (FDC) and Negative Slope Coefficient (NSC). The second group are Predictors of Expected Performance (PEP), models that take as input a set of descriptive attributes of a problem and predict the expected performance of GP. This paper compares an EI, the NSC, and a PEP model for a GP classifier. Results suggest that the EI does not correlate with the performance of the GP classifiers. Conversely, the PEP models show a high correlation with GP performance.",
-
notes = "Also known as \cite{2331006} Distributed at
GECCO-2012.
ACM Order Number 910122.",
