Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Solution algorithms for fuzzy relational equations with max-product composition
Fuzzy Sets and Systems
Solving fuzzy relation equations with a linear objective function
Fuzzy Sets and Systems
Optimization of fuzzy relation equations with max-product composition
Fuzzy Sets and Systems
Solving nonlinear optimization problems with fuzzy relation equation constraints
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Multi-objective optimization problems with fuzzy relation equation constraints
Fuzzy Sets and Systems - Special issue: Optimization and decision support systems
Optimization of fuzzy relational equations with max-av composition
Information Sciences: an International Journal
Infinite fuzzy relation equations with continuous t-norms
Information Sciences: an International Journal
System of fuzzy relation equations with inf-→ composition: Complete set of solutions
Fuzzy Sets and Systems
Information Sciences: an International Journal
On the relation between equations with max-product composition and the covering problem
Fuzzy Sets and Systems
Mathematical and Computer Modelling: An International Journal
A hybrid OC-GA approach for fast and global truss optimization with frequency constraints
Applied Soft Computing
A multi-objective evolutionary approach for fuzzy optimization in production planning
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In the present paper, a genetic algorithm for multi-objective optimization problems with max-product fuzzy relation equations as constraints is presented. Since the non-empty feasible domain of such problems is, in general, a non-convex set; the traditional optimization methods cannot be applied. Here, we are presenting a genetic algorithm (GA) to find ''Pareto optimal solutions'' for solving such problems observing the role of non-convexity of the feasible domain of decision problem. Solutions are kept within feasible region during the mutation as well as crossover operations. Test problems are developed to evaluate the performance of the proposed algorithm and to determine satisficing decisions. In case of two objectives, weighting method is also applied to find the locus of optimal solutions.