New topologies for genetic search space

We propose three distance measures for genetic search space. One is a distance measure in the population space that is useful for understanding the working mechanism of genetic algorithms. Another is a distance measure in the solution space for K-grouping problems. This can be used for normalization in crossover. The third is a level distance measure for genetic algorithms, which is useful for measuring problem difficulty with respect to genetic algorithms. We show that the proposed measures are metrics and the measures are efficiently computed. Categories and Subject Descriptors G.2.3 [Mathematics of Computing]: DISCRETE MATHEMATICS—Applications General Terms Theory Keywords Distance mesaure, genetic algorithms

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Yong-Hyuk Kim, Byung Ro Moon
• Keywords: Distance Measure, GECCO 2005, Genetic Algorithms, Genetic Search Space