iECGA: Integer extended compact genetic algorithm

Abstract

Extended compact genetic algorithm (ECCA) is an algorithm that can solve hard problems in the binary domain. ECCA is reliable and accurate because of the capability of detecting building blocks, but certain difficulties are encountered when we directly apply ECGA to problems in the integer domain. In this paper, we propose a new algorithm that extends ECGA, called integer extended compact genetic algorithm (iECCA). iECGA uses a modified probability model and inherits the capability of detecting building blocks from ECGA. iECGA is specifically designed for problems in the integer domain and can avoid the difficulties that ECGA encounters. With the experimental results, we show the performance comparisons between ECCA, iECGA, and a simple GA. The results indicate that iECGA has good performance on problems in the integer domain.