Journal ArticleDOI
Evolutionary algorithms in control systems engineering: a survey
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TLDR
In this paper, the important role of evolutionary algorithms in multi-objective optimisation is highlighted, and evolutionary advances in adaptive control and multidisciplinary design are predicted, as well as significant applications in parameter and structure optimisation for controller design and model identification, in addition to fault diagnosis, reliable systems, robustness analysis, and robot control.About:
This article is published in Control Engineering Practice.The article was published on 2002-11-01. It has received 612 citations till now. The article focuses on the topics: Adaptive control & Evolutionary algorithm.read more
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Metaheuristics: From Design to Implementation
TL;DR: This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling.
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Machine Learning for Fluid Mechanics
TL;DR: An overview of machine learning for fluid mechanics can be found in this article, where the strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation.
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Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
Steven L. Brunton,J. Nathan Kutz +1 more
TL;DR: In this paper, the authors bring together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science, and highlight many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy.
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Dynamic multiobjective optimization problems: test cases, approximations, and applications
TL;DR: A suite of five test problems offering different patterns of such changes and different difficulties in tracking the dynamic Pareto-optimal front by a multiobjective optimization algorithm is presented.
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Closed-Loop Turbulence Control: Progress and Challenges
Steven L. Brunton,Bernd R. Noack +1 more
References
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Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Book
Genetic Programming: On the Programming of Computers by Means of Natural Selection
TL;DR: This book discusses the evolution of architecture, primitive functions, terminals, sufficiency, and closure, and the role of representation and the lens effect in genetic programming.
Book
Genetic Algorithms + Data Structures = Evolution Programs
TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.