Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Illustrating evolutionary computation with Mathematica
Illustrating evolutionary computation with Mathematica
Genetic Algorithms and Fuzzy Multiobjective Optimization
Genetic Algorithms and Fuzzy Multiobjective Optimization
Simulating Neural Networks with Mathematica
Simulating Neural Networks with Mathematica
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Convex Optimization
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
Practical Bilevel Optimization: Algorithms and Applications (Nonconvex Optimization and Its Applications)
Fuzzy and Multiobjective Games for Conflict Resolution (Studies in Fuzziness and Soft Computing)
Fuzzy and Multiobjective Games for Conflict Resolution (Studies in Fuzziness and Soft Computing)
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Fuzzy multiobjective optimization modeling with mathematica
WSEAS TRANSACTIONS on SYSTEMS
Fuzzy multiobjective bimatrix games: introduction to the computational techniques
ISTASC'09 Proceedings of the 9th WSEAS International Conference on Systems Theory and Scientific Computation
Solving non-linear equations via genetic algorithms
EC'05 Proceedings of the 6th WSEAS international conference on Evolutionary computing
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Genetic stochastic search algorithms (GAs) have soon demonstrated their helpful contribution for finding solutions to the complex real-life optimization problems. These algorithms have been applied extensively for solving Nash equilibria of fuzzy bimatrix games with single objective. The experience shows the ability of the GAs to find solutions to equivalent nonlinear programming problems, without an exhaustive search and computing gradients. This paper is an attempt to handle the complexity of the real-life situations, when the decision makers are facing to multiple objectives in a fuzzy environment. The hybridation of GA and classical local optimization techniques is also suggested. The software MATHEMATICA 7.0.1 and the GA-based optimization package GENOCOP III are used to implement these techniques within a high-performance computing environment.