Complexity-based fitness evaluation

author = "Hitoshi Iba",

title = "Complexity-based fitness evaluation",

booktitle = "Handbook of Evolutionary Computation",

publisher = "Oxford University Press",

publisher_2 = "Institute of Physics Publishing",

year = "1997",

editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz",

chapter = "section C4.4",

keywords = "genetic algorithms, genetic programming",

ISBN = "0-7503-0392-1",

URL = "

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf",

DOI = "

doi:10.1201/9781420050387.ptc",

size = "8 pages",

abstract = "This section describes the complexity-based fitness evaluation for evolutionary algorithms. We first introduce and compare the leading competing model selection criteria, namely, an MDL (minimum-description-length) principle, the AIC (Akaike information criterion), an MML (minimum-message-length) principle, the PLS (predictive least-squares) measure, cross-validation, and the maximum-entropy principle. Then we give an illustrative example to show the effectiveness of the complexity-based fitness by experimenting with evolving decision trees using genetic programming (GP). Thereafter, we describe various research on complexity-based fitness evaluation, that is, controlling genetic algorithm or GP search strategies by means of the MDL criterion.",