an analysis of multi-chromosome gas in deceptive problems
This paper discusses a new approach to using GAs to solve deceptive fitness landscapes by incorporating mechanisms to control the convergence direction instead of simply increasing the population diversity. In order to overcome some of the difficulties that GAs face when searching deceptive landscapes, we introduce two new multi-chromosome genetic algorithms. These multi-chromosome genetic algorithms have been designed to accelerate the GA's search speed in more complicated deceptive problems by looking for a balance between diversity and convergence. Five different problems are used in testing to illustrate the usefulness of our proposed approaches. The results show that the lack of diversity is not the only reason that normal GAs have difficulty in solving deceptive problems but that convergence direction is also important.
deceptive problems diversity genetic algorithms empirical analysis multi-chromosome representations