Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
The Practical Handbook of Genetic Algorithms: Applications, Second Edition
The Practical Handbook of Genetic Algorithms: Applications, Second Edition
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
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
Computers and Operations Research
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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The EWMA quality control chart, and its multivariate version (MEWMA), may be designed to efficiently detect small shifts in the mean vector of a set of p quality characteristics of a production process. However, this work presents a method for the optimal design of the parameters of the MEWMA and EWMA charts to control processes where it is not convenient to detect small magnitude shifts and, at the same time, powerful enough to detect shifts considered important. This problem can be considered as a multiobjective optimization where two regions of different performance are defined. The objective of this paper is to find the best MEWMA and EWMA quality control charts given the previous regions, where the requirements for each region has to be balanced to decide which solution is better. For this purpose, friendly Windows software has been developed to optimize this problem, using Genetic Algorithms. Results show that the design using our approach outperforms the other designs.