Evolutionary algorithms in control systems engineering: a survey

By Peter John Fleming, Robin Charles Purshouse. 2002

663 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/f8a9b2ba87c9282b94bf2a99820539e3032ccf48

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

Citations in the corpus

From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis (2013, neutral): “Some researchers applied multi-objective optimization to solve this problem [12], [33].”
Machine Learning for Fluid Mechanics (2019, neutral): “Genetic programming has been used extensively in active control for engineering applications (Dracopoulos 1997; Fleming & Purshouse 2002) and in recent years in several ﬂow control plants.”
Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles (2019, background): “EVOLUTIONARY algorithms (EAs) have been shown to be effective in a number of real-world optimization applications [1], [2].”
Data-Driven Evolutionary Optimization: An Overview and Case Studies (2019, background): “Over the past decades, evolutionary algorithms (EAs) have become a popular tool for optimization [4], [5].”
Advanced control in factory automation: a survey (2017, background): “Moreover, EAs may be typically employed in the optimisation of fuzzy or neural controllers (Fleming and Purshouse 2002).”
Dynamic multiobjective optimization problems: test cases, approximations, and applications (2004, background): “Optimal design of controllers is a classical field of application for evolutionary computation (see [6] for a review of applications) and evolutionary multiobjective optimization.”
Machine Learning Control – Taming Nonlinear Dynamics and Turbulence (2016, background): “Fleming & Purshouse [103] summarize: ‘The evolutionary computing (EC) ﬁeld has its origins in four landmark evolutionary approaches: evolutionary programming (EP) (Fogel, Owens, & Walsh, 1966), evolution strategies (ES) (Schwefel, 1965; Rechenberg, 1973), genetic algorithms (GA) (Holland, 1975),…”
Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems (2017, methodology): “AS A POWERFUL optimization tool, evolutionary algorithms (EAs) are able to solve problems that cannot be easily handled by conventional mathematical programming techniques and have found many successful real-world applications [1].”
A hybrid genetic algorithm and bacterial foraging approach for global optimization (2007, background): “In the last decade, approaches based on genetic algorithms (GA) have received increased attention from the academic and industrial communities for dealing with optimization problems that have been shown to be intractable using conventional problem solving techniques [10, 13 ,14,20,25,31,32,34].”
Data-Driven Surrogate-Assisted Multiobjective Evolutionary Optimization of a Trauma System (2016, background): “For these reasons, EAs ha ve enjoyed great success in solving a wide range of industrial problems [1], [2].”
A Method for Handling Uncertainty in Evolutionary Optimization With an Application to Feedback Control of Combustion (2009, neutral): “controller optimization is given in [25].”