CrossNet: a framework for crossover with network-based chromosomal representations

We propose a new class of crossover operators for genetic algorithms (CrossNet) which use a network-based (or graphbased) chromosomal representation. We designed CrossNet with the intent of providing a framework for creating crossover operators that take advantage of domain-specific knowledge for solving problems. Specifically, GA users supply a network which defines the epistatic relationships between genes in the genotype. CrossNet-based crossover uses this information with the goal of improving linkage. We performed two experiments that compared CrossNet-based crossover with one-point and uniform crossover. The first experiment involved the density classification problem for cellular automata (CA), and the second experiment involved fitting two randomly generated hyperplane-defined functions (hdf’s). Both of these exploratory experiments support the hypothesis that CrossNet-based crossover can be useful, although performance improvements were modest. We discuss the results...

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Forrest Stonedahl, William Rand, Uri Wilensky
• Keywords: CrossNet-based Crossover, Crossover, Crossover Operators, GECCO 2008, Optimization