Foundations of genetic programming
Foundations of genetic programming
On classes of functions for which No Free Lunch results hold
Information Processing Letters
Focused no free lunch theorems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Two broad classes of functions for which a no free lunch result does not hold
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
No free lunch, program induction and combinatorial problems
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Free lunches for neural network search
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
Open issues in genetic programming
Genetic Programming and Evolvable Machines
Practical performance models of algorithms in evolutionary program induction and other domains
Artificial Intelligence
Estimating classifier performance with genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Runtime analysis of mutation-based geometric semantic genetic programming on boolean functions
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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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.