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A Method for Handling Uncertainty in Evolutionary Optimization with an Application to Feedback Control of Combustion
"... Abstract — We present a novel method for handling uncertainty in evolutionary optimization. The method entails quantification and treatment of uncertainty and relies on the rank based selection operator of evolutionary algorithms. The proposed uncertainty handling is implemented in the context of th ..."
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Abstract — We present a novel method for handling uncertainty in evolutionary optimization. The method entails quantification and treatment of uncertainty and relies on the rank based selection operator of evolutionary algorithms. The proposed uncertainty handling is implemented in the context of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and verified on test functions. The present method is independent of the uncertainty distribution, prevents premature convergence of the evolution strategy and is well suited for online optimization as it requires only a small number of additional function evaluations. The algorithm is applied in an experimental set-up to the online optimization of feedback controllers of thermoacoustic instabilities of gas turbine combustors. In order to mitigate these instabilities, gain-delay or model-based H ∞ controllers sense the pressure and command secondary fuel injectors. The parameters of these controllers are usually specified via a trial and error procedure. We demonstrate that their online optimization with the proposed methodology enhances, in an automated fashion, the online performance of the controllers, even under highly unsteady operating conditions, and it also compensates for uncertainties in the model-building and design process. I.
A review of the application of Multi-Objective Evolutionary Fuzzy systems: Current status and further directions
- IEEE Trans. Fuzzy Syst
"... Abstract—Over the past few decades, fuzzy systems have been widely used in several application fields, thanks to their ability to model complex systems. The design of fuzzy systems has been successfully performed by applying evolutionary and, in particular, genetic algorithms, and recently, this app ..."
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Abstract—Over the past few decades, fuzzy systems have been widely used in several application fields, thanks to their ability to model complex systems. The design of fuzzy systems has been successfully performed by applying evolutionary and, in particular, genetic algorithms, and recently, this approach has been extended by using multiobjective evolutionary algorithms, which can consider multiple conflicting objectives, instead of a single one. The hybridization between multiobjective evolutionary algorithms and fuzzy systems is currently known as multiobjective evolutionary fuzzy systems. This paper presents an overview of multiobjective evolutionary fuzzy systems, describing the main contributions on this field and providing a two-level taxonomy of the existing proposals, in order to outline a well-established framework that could help researchers who work on significant further developments. Finally, some considerations of recent trends and potential research directions are presented. Index Terms—Accuracy–interpretability tradeoff, fuzzy association rule mining, fuzzy control, fuzzy rule-based systems (FRBSs), multiobjective evolutionary algorithms (EAs), multiobjective evolutionary fuzzy systems (MOEFSs). I.
Biogeography-based optimization for robot controller tuning
- in Computational Modeling and Simulation of Intellect: Current State and Future Perspectives. IGI Global
, 2011
"... ABSTRACT This research involves the development of an engineering test for a newly-developed evolutionary algorithm called biogeography-based optimization (BBO), and also involves the development of a distributed implementation of BBO. The BBO algorithm is based on mathematical models of biogeograp ..."
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ABSTRACT This research involves the development of an engineering test for a newly-developed evolutionary algorithm called biogeography-based optimization (BBO), and also involves the development of a distributed implementation of BBO. The BBO algorithm is based on mathematical models of biogeography, which describe the migration of species between habitats. BBO is the adaptation of the theory of biogeography for the purpose of solving general optimization problems. In this research, BBO is used to tune a proportional-derivative control system for real-world mobile robots. We have shown that BBO can successfully tune the control algorithm of the robots, reducing their tracking error cost function by 65% from nominal values. This chapter focuses on describing the hardware, software and the results that have been obtained by various implementations of BBO.
Real-time Evolution of an Embedded Controller for an Autonomous Helicopter
- In Proc. 2008 IEEE Congress on Evolutionary Computation (CEC 2008), Hong Kong
, 2008
"... Abstract — In this paper we evolve the parameters of a proportional, integral, and derivative (PID) controller for an unstable, complex and nonlinear system. The individuals of the applied genetic algorithm (GA) are evaluated on the actual system rather than on a simulation of it. This makes implici ..."
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Abstract — In this paper we evolve the parameters of a proportional, integral, and derivative (PID) controller for an unstable, complex and nonlinear system. The individuals of the applied genetic algorithm (GA) are evaluated on the actual system rather than on a simulation of it. This makes implicit a formal model identification for the implementation of a simulator. This also calls for the GA to be approached in an unusual way, where we need to consider new aspects not normally present in the usual situations using an unnaturally consistent simulator for fitness evaluation. Although elitism is used in the GAs, no monotonic increase in fitness is exhibited by the algorithm. Instead, we show that the GA’s individuals converge towards more robust solutions. I.
Reverse-engineering of Artificially Evolved Controllers for Swarms of Robots
- in IEEE Congress on Evolutionary Computation
, 2009
"... Abstract — It is generally challenging to design decentralized controllers for swarms of robots because there is often no obvious relation between the individual robot behaviors and the final behavior of the swarm. As a solution, we use artificial evolution to automatically discover neural controlle ..."
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Abstract — It is generally challenging to design decentralized controllers for swarms of robots because there is often no obvious relation between the individual robot behaviors and the final behavior of the swarm. As a solution, we use artificial evolution to automatically discover neural controllers for swarming robots. Artificial evolution has the potential to find simple and efficient strategies which might otherwise have been overlooked by a human designer. However, evolved controllers are often unadapted when used in scenarios that differ even slightly from those encountered during the evolutionary process. By reverse-engineering evolved controllers we aim towards handdesigned controllers which capture the simplicity and efficiency of evolved neural controllers while being easy to optimize for a variety of scenarios. I.
ADVANCES IN MODEL–BASED FAULT DIAGNOSIS WITH EVOLUTIONARY ALGORITHMS AND NEURAL NETWORKS
"... Challenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, the classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as evolutionary algorithms and neural networks become ..."
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Challenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, the classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as evolutionary algorithms and neural networks become more and more popular in industrial applications of fault diagnosis. The main objective of this paper is to present recent developments regarding the application of evolutionary algorithms and neural networks to fault diagnosis. In particular, a brief introduction to these computational intelligence paradigms is presented, and then a review of their fault detection and isolation applications is performed. Close attention is paid to techniques that integrate the classical and soft computing methods. A selected group of them is carefully described in the paper. The performance of the presented approaches is illustrated with the use of the DAMADICS fault detection benchmark that deals with a valve actuator.
Exploring New Horizons in Evolutionary Design of Robots
- in "CD Proceedings of IROS 2009 Workshop on Exploring new horizons in Evolutionary Design of Robots (Evoderob09
"... Robots ” considers the field of Evolutionary Robotics (ER) from the perspective of its potential users: roboticists. The core hypothesis motivating this field of research will be discussed, as well as the potential use of ER in a robot design process. Three main aspects of ER will be presented: (a) ..."
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Robots ” considers the field of Evolutionary Robotics (ER) from the perspective of its potential users: roboticists. The core hypothesis motivating this field of research will be discussed, as well as the potential use of ER in a robot design process. Three main aspects of ER will be presented: (a) ER as an automatic parameter tuning procedure, which is the most mature application and is used to solve real robotics problem, (b) evolutionary-aided design, which may benefit the designer as an efficient tool to build robotic systems and (c) automatic synthesis, which corresponds to the automatic design of a mechatronic device. Critical issues will also be presented as well as current trends and pespectives in ER. I.
A Method of Accelerating Convergence for Genetic Algorithms Evolving Morphological and Control Parameters for a Biomimetic Robot
"... Abstract — In generating efficient gaits for biomimetic robots, control commands and robot morphology are closely coupled, particularly for soft bodied robots with complex internal dynamics. Achieving optimal robot energy consumption is only possible if robot control parameters and morphology are tu ..."
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Abstract — In generating efficient gaits for biomimetic robots, control commands and robot morphology are closely coupled, particularly for soft bodied robots with complex internal dynamics. Achieving optimal robot energy consumption is only possible if robot control parameters and morphology are tuned simultaneously. Genetic Algorithms (GAs) are well suited for this purpose. In this application, however, GAs converge slowly because of the high dimensionality of the fitness landscape, the limited number of successful designs within this landscape, and the significant computational cost of evaluating the fitness function using dynamics simulations. To accelerate GA convergence for design applications involving biomimetic robots, a new physics-based preprocessing methodology is proposed. This preprocessing strategy was applied to develop gaits for a biomimetic caterpillar robot. Convergence speeds were observed to increase significantly through the application of the physicsbased preprocessing. U I.
2006b). Clearance of nonlinear flight control laws using hybrid evolutionary optimisation
- IEEE Transactions on Evolutionary Computation
"... The application of two evolutionary optimisation methods, namely differential evolution and genetic algorithms, to the clearance of nonlinear flight control laws for highly augmented aircraft is described. The algorithms are applied to the problem of evaluating a nonlinear handling qualities clearan ..."
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The application of two evolutionary optimisation methods, namely differential evolution and genetic algorithms, to the clearance of nonlinear flight control laws for highly augmented aircraft is described. The algorithms are applied to the problem of evaluating a nonlinear handling qualities clearance criterion for a simulation model of a high performance aircraft with a delta canard configuration and a full-authority flight control law. Hybrid versions of both algorithms, incorporating local gradient-based optimisation, are also developed and evaluated. Statistical comparisons of computational complexity and global convergence properties reveal the benefits of hybridisation for both algorithms. The differential evolution approach in particular, when appropriately augmented with local optimisation methods, is shown to have significant potential for improving both the reliability and efficiency of the current industrial flight clearance process.
