Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{oltean_bioadit_springer2004, author = "Mihai Oltean", title = "Searching for a Practical Evidence for the No Free Lunch Theorems", booktitle = "Biologically Inspired Approaches to Advanced Information Technology: First International Workshop, BioADIT 2004", year = "2004", editor = "Auke Jan Ijspeert and Masayuki Murata and Naoki Wakamiya", volume = "3141", series = "LNCS", pages = "472--483", address = "Lausanne, Switzerland", month = "29-30 " # jan, publisher = "Springer-Verlag", note = "Revised Selected Papers", email = "moltean@cs.ubbcluj.ro", keywords = "genetic algorithms, genetic programming, No Free Lunch, NFL, pairs of algorithms", ISBN = "3-540-23339-3", ISSN = "0302-9743", URL = "http://www.cs.ubbcluj.ro/~moltean/oltean_bioadit_springer2004.pdf", URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3141&spage=472", DOI = "doi:10.1007/b101281", size = "12 pages", abstract = "According to the No Free Lunch (NFL) theorems all blackbox algorithms perform equally well when compared over the entire set of optimisation problems. An important problem related to NFL is finding a test problem for which a given algorithm is better than another given algorithm. Of high interest is finding a function for which Random Search is better than another standard evolutionary algorithm. In this paper we propose an evolutionary approach for solving this problem: we will evolve test functions for which a given algorithm A is better than another given algorithm B. Two ways for representing the evolved functions are employed: as GP trees and as binary strings. Several numerical experiments involving NFL-style Evolutionary Algorithms for function optimization are performed. The results show the effectiveness of the proposed approach. Several test functions for which Random Search performs better than all other considered algorithms have been evolved.", }

Genetic Programming entries for Mihai Oltean