Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Foundations of genetic programming
Foundations of genetic programming
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
A Taxonomy of Evolutionary Algorithms in Combinatorial Optimization
Journal of Heuristics
Practical model of genetic programming's performance on rational symbolic regression problems
EuroGP'08 Proceedings of the 11th 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
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Hi-index | 0.01 |
A few attempts to create taxonomies in evolutionary computation have been made. These either group algorithms or group problems on the basis of their similarities. Similarity is typically evaluated by manually analysing algorithms/problems to identify key characteristics that are then used as a basis to form the groups of a taxonomy. This task is not only very tedious but it is also rather subjective. As a consequence the resulting taxonomies lack universality and are sometimes even questionable. In this paper we present a new and powerful approach to the construction of taxonomies and we apply it to Genetic Programming (GP). Only one manually constructed taxonomy of problems has been proposed in GP before, while no GP algorithm taxonomy has ever been suggested. Our approach is entirely automated and objective. We apply it to the problem of grouping GP systems with their associated parameter settings. We do this on the basis of performance signatures which represent the behaviour of each system across a class of problems. These signatures are obtained thorough a process which involves the instantiation of models of GP's performance. We test the method on a large class of Boolean induction problems.