An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
A Micro-Genetic Algorithm for Multiobjective Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Dynamic multiobjective evolutionary algorithm: adaptive cell-based rank and density estimation
IEEE Transactions on Evolutionary Computation
Rank-density-based multiobjective genetic algorithm and benchmark test function study
IEEE Transactions on Evolutionary Computation
Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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This study describes the application of intelligent control systems in electrospinning engineering as well as to use these approaches for optimizing processing conditions. A multi-objective optimization method based on gene algorithm GA has been proposed for the design and control of electrospinning process. The processing parameters including Polyvinyl alcohol PVA solution concentration, applied voltage, spinning distance and volume flow rate were used as design variables and were mathematically related with the electrospun fiber properties fiber diameter and its distribution using gene expression programming GEP technique. Nonlinear mathematical functions were derived based on the processing parameters. Afterward, using a multi-objective optimization technique based on gene algorithm, optimal conditions were found in such a way that, mean electrospun fiber diameter and its distribution to be minimized.