Computational complexity analysis of simple genetic programming on two problems modeling isolated program semantics

Analyzing the computational complexity of evolutionary algorithms (EAs) for binary search spaces has significantly informed our understanding of EAs in general. With this paper, we start the computational complexity analysis of genetic programming (GP). We set up several simplified GP algorithms and analyze them on two separable model problems, ORDER and MAJORITY, each of which captures a relevant facet of typical GP problems. Both analyses give first rigorous insights into aspects of GP design, highlighting in particular the impact of accepting or rejecting neutral moves and the importance of a local mutation operator. Categories and Subject Descriptors F.2 [Theory of Computation]: Analysis of algorithms and problem complexity General Terms Algorithms, Theory

• Type: Journal
• Year: 2011
• Where: FOGA
• Authors: Greg Durrett, Frank Neumann, Una-May O'Reilly
• Keywords: Artificial Intelligence, Complexity Analysis, FOGA 2011, Mutation Operator, Problem Complexity