Computational Complexity Analysis of Simple Genetic Programming On Two Problems Modeling Isolated Program Semantics
Neural and Evolutionary Computing
2010-11-16 v2 Computational Complexity
Data Structures and Algorithms
Abstract
Analyzing the computational complexity of evolutionary algorithms for binary search spaces has significantly increased their theoretical understanding. With this paper, we start the computational complexity analysis of genetic programming. We set up several simplified genetic programming algorithms and analyze them on two separable model problems, ORDER and MAJORITY, each of which captures an important facet of typical genetic programming problems. Both analyses give first rigorous insights on aspects of genetic programming design, highlighting in particular the impact of accepting or rejecting neutral moves and the importance of a local mutation operator.
Cite
@article{arxiv.1007.4636,
title = {Computational Complexity Analysis of Simple Genetic Programming On Two Problems Modeling Isolated Program Semantics},
author = {Greg Durrett and Frank Neumann and Una-May O'Reilly},
journal= {arXiv preprint arXiv:1007.4636},
year = {2010}
}
Comments
26 pages