Many evolutionary algorithms have been proposed for large scale optimization. Parameter interaction in non-separable problems is a major source of performance loss specially on large scale problems. Cooperative Co-evolution(CC) has been proposed as a natural solution for large scale optimization problems, but lack of a systematic way of decomposing large scale non-separable problems is a major obstacle for CC frameworks. The aim of this paper is to propose a systematic way of capturing interacting variables for a more effective problem decomposition suitable for cooperative co-evolutionary frameworks. Grouping interacting variables in different subcomponents in a CC framework imposes a limit to the extent interacting variables can be optimized to their optimum values, in other words it limits the improvement interval of interacting variables. This is the central idea of the newly proposed technique which is called delta method. Delta method measures the averaged difference in a certain variable across the entire population and uses it for identifying interacting variables. The experimental results show that this new technique is more effective than the existing random grouping method. © 2010 IEEE.
|Title of host publication
|2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
|Published - Jul 2010