Paper Info
Title
Free lunches for function and program induction
Abstract
In this paper we prove that for a variety of practical problems and representations, there is a free lunch for search algorithms that specialise in the task of finding functions or programs that solve problems, such as genetic programming. In other words, not all such algorithms are equally good under all possible performance measures. We focus in particular on the case where the objective is to discover functions that fit sets of data-points - a task that we will call symbolic regression. We show under what conditions there is a free lunch for symbolic regression, highlighting that these are extremely restrictive.
Year
DOI
Venue
2009
10.1145/1527125.1527148
FOGA
Keywords
Field
DocType
program induction,possible performance measure,search algorithm,practical problem,genetic programming,free lunch,fit set,symbolic regression,no free lunch,theory
Mathematical optimization,Search algorithm,Computer science,No free lunch in search and optimization,Genetic programming,Artificial intelligence,Symbolic regression,Machine learning
Conference
Citations 
PageRank 
References 
14
0.68
6
Authors
3
Name
Order
Citations
PageRank
Riccardo Poli12589308.79
Mario Graff212521.24
Nicholas Freitag McPhee340432.94