Interactive genetic algorithms with individual's fuzzy fitness

Authors:
Dun-wei Gong;Jie Yuan;Xiao-yan Sun
Affiliations:
School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China;School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China;School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China
Venue:
Computers in Human Behavior
Year:
2011

Citing 6
Cited 4

An analysis of cooperative coevolutionary algorithms

An analysis of cooperative coevolutionary algorithms
An interactive genetic algorithm-based framework for handling qualitative criteria in design optimization

Computers in Industry
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation
Interactive Evolutionary Computation-Based Hearing Aid Fitting

IEEE Transactions on Evolutionary Computation
Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms

IEEE Transactions on Evolutionary Computation
Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria Using Interactive Genetic Algorithms

IEEE Transactions on Evolutionary Computation

Evolutionary algorithms for optimization problems with uncertainties and hybrid indices

Information Sciences: an International Journal
Application of variational granularity language sets in interactive genetic algorithms

ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Fuzzy Assessment of Health Information System Users' Security Awareness

Journal of Medical Systems
Crossover method for interactive genetic algorithms to estimate multimodal preferences

Applied Computational Intelligence and Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Interactive genetic algorithms are effective methods to solve an optimization problem with implicit or fuzzy indices, and have been successfully applied to many real-world optimization problems in recent years. In traditional interactive genetic algorithms, many researchers adopt an accurate number to express an individual's fitness assigned by a user. But it is difficult for this expression to reasonably reflect a user's fuzzy and gradual cognitive to an individual. We present an interactive genetic algorithm with an individual's fuzzy fitness in this paper. Firstly, we adopt a fuzzy number described with a Gaussian membership function to express an individual's fitness. Then, in order to compare different individuals, we generate a fitness interval based on @a-cut set, and obtain the probability of individual dominance by use of the probability of interval dominance. Finally, we determine the superior individual in tournament selection with size two based on the probability of individual dominance, and perform the subsequent evolutions. We apply the proposed algorithm to a fashion evolutionary design system, a typical optimization problem with an implicit index, and compare it with two interactive genetic algorithms, i.e., an interactive genetic algorithm with an individual's accurate fitness and an interactive genetic algorithm with an individual's interval fitness. The experimental results show that the proposed algorithm is advantageous in alleviating user fatigue and looking for user's satisfactory individuals.