Diversity as a selection pressure in dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Multi-chromosomal genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
GARS: an improved genetic algorithm with reserve selection for global optimization
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Theoretical analysis of diversity mechanisms for global exploration
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Solving discrete deceptive problems with EMMRS
Proceedings of the 10th annual conference on Genetic and evolutionary computation
The underlying similarity of diversity measures used in evolutionary computation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Analysis of a triploid genetic algorithm over deceptive landscapes
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Analysis of a triploid genetic algorithm over deceptive and epistatic landscapes
ACM SIGAPP Applied Computing Review
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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.