Skip to main content
Article thumbnail
Location of Repository

A Feasibility-preserving Crossover and Mutation Operator for Constrained Combinatorial Problems

By Martin Lukasiewycz, Michael Glaß and Jürgen Teich

Abstract

Abstract. This paper presents a feasibility-preserving crossover and mutation operator for evolutionary algorithms for constrained combinatorial problems. This novel operator is driven by an adapted Pseudo-Boolean solver that guarantees feasible offspring solutions. Hence, this allows the evolutionary algorithm to focus on the optimization of the objectives instead of searching for feasible solutions. Based on a proposed scalable testsuite, six specific testcases are introduced that allow a sound comparison of the feasibility-preserving operator to known methods. The experimental results show that the introduced approach is superior to common methods and competitive to a recent state-of-the-art decoding technique. 1 Introduction and Related Work Definition 1. A constrained combinatorial problem is defined as: minimize f(x) subject to x ∈ Xf with Xf ⊆ {0, 1} n The objective function f allows multi-dimensional and non-linear calculations. Th

Year: 2010
OAI identifier: oai:CiteSeerX.psu:10.1.1.161.4047
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www12.informatik.uni-er... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.