Further contributions to the study of finite fuzzy relations equations
Fuzzy Sets and Systems
Processing in relational structures: fuzzy relational equations
Fuzzy Sets and Systems - Special memorial volume on foundations of fuzzy reasoning
Evolving artificial intelligence
Evolving artificial intelligence
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (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
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Relation Equations and Their Applications to Knowledge Engineering
Fuzzy Relation Equations and Their Applications to Knowledge Engineering
Multi-objective optimization problems with fuzzy relation equation constraints
Fuzzy Sets and Systems - Special issue: Optimization and decision support systems
On the relation between equations with max-product composition and the covering problem
Fuzzy Sets and Systems
An accelerated approach for solving fuzzy relation equations with a linear objective function
IEEE Transactions on Fuzzy Systems
Computers and Industrial Engineering
Satisficing solutions of multi-objective fuzzy optimization problems using genetic algorithm
Applied Soft Computing
An algorithm for solving optimization problems with fuzzy relational inequality constraints
Information Sciences: an International Journal
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In this paper a nonlinear objective optimization model subject to a system of fuzzy relation equations with max-average composition are presented. When the set of solutions of fuzzy relation equations is not empty, it is in general a non-convex set and so the conventional nonlinear programming methods are not ideal for solving such a problem. In order to solve this problem, a modified genetic algorithm is reviewed and some of its components are changed to solve the problem. The construction of test problems is also developed to evaluate the performance of the proposed algorithm.