Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
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.)
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
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
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Multiobjective optimization strategy so-called Physical Programming allows controller designers a flexible way to express design preferences with a 'physical' sense. For each objective (settling time, overshoot, disturbance rejection, etc.) preferences are established through categories as desirable, tolerable, unacceptable, etc. assigned to numerical ranges. The problem is translated into a unique objective optimization but normally as a multimodal problem. This work shows how to convert a robust control design problem into a multiobjective optimization problem and to solve it by Physical Programming and Genetic Algorithms. An application to the American Control Conference (ACC) Robust Control Benchmark is presented and compared with other known solutions.