In order to be able to predict the maximum Annual Energy Production (AEP) for tidal power plants, an AEP optimization tool based on evolutionary algorithms was developed by ANDRITZ HYDRO. This tool can simulate all op...
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This paper discusses the concept of ageing as this applies to the operation of evolutionary algorithms, and examines its relationship to the concept of ageing as this is understood for human beings. evolutionary Algor...
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This paper discusses the concept of ageing as this applies to the operation of evolutionary algorithms, and examines its relationship to the concept of ageing as this is understood for human beings. evolutionary algorithms constitute a family of search algorithms which base their operation on an analogy from the evolution of species in nature. The paper initially provides the necessary knowledge on the operation of evolutionary algorithms, focusing on the use of ageing strategies during the implementation of the evolutionary process. Background knowledge on the concept of ageing, as this is defined scientifically for biological systems, is subsequently presented. Based on this information, the paper provides a comparison between the two ageing concepts, and discusses the philosophical inspirations which can be drawn for human ageing based on the operation of evolutionary algorithms. (C) 2017 Elsevier B.V. All rights reserved.
In this work, we propose a novel method for a supervised dimensionality reduc- tion, which learns weights of a neural network using an evolutionary algorithm, CMA-ES, optimising the success rate of the k-NN classifier...
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In this work, we propose a novel method for a supervised dimensionality reduc- tion, which learns weights of a neural network using an evolutionary algorithm, CMA-ES, optimising the success rate of the k-NN classifier. If no activation func- tions are used in the neural network, the algorithm essentially performs a linear transformation, which can also be used inside of the Mahalanobis distance. There- fore our method can be considered to be a metric learning algorithm. By adding activations to the neural network, the algorithm can learn non-linear transfor- mations as well. We consider reductions to low-dimensional spaces, which are useful for data visualisation, and demonstrate that the resulting projections pro- vide better performance than other dimensionality reduction techniques and also that the visualisations provide better distinctions between the classes in the data thanks to the locality of the k-NN classifier. 1
evolutionary algorithms are optimization methods commonly used to solve engineering and business optimization problems. The parameters in evolutionary algorithm must be perfectly tuned in a way that the optimization a...
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evolutionary algorithms are optimization methods commonly used to solve engineering and business optimization problems. The parameters in evolutionary algorithm must be perfectly tuned in a way that the optimization algorithm solves the optimization problems efficiently and effectively. Several parameter tuning approaches with a single performance metric have been proposed in the literature. However, simultaneous consideration of multiple performance metrics could provide the optimal setting for the parameters in the evolutionary algorithm. In this research, a new hybrid parameter tuning approach is proposed to simultaneously optimize the performance metrics of the evolutionary optimization algorithm while it is used in solving an optimization problem. The proposed hybrid approach provides the optimal value of parameters of the evolutionary optimization algorithm. The proposed approach is the first parameter tuning approach in the evolutionary optimization algorithm which simultaneously optimizes all performance metrics of the evolutionary optimization algorithm. To do this, a full factorial design of experiment is used to find the significant parameters of the evolutionary optimization algorithm, as well as an approximate equation for each performance metric. The individual and composite desirability function approaches are then proposed to provide the optimal setting for the parameters of the evolutionary optimization algorithm. For the first time, we use the desirability function approach to find an optimal level for the parameters in the evolutionary optimization algorithm. To show the real application of the proposed parameter tuning approach, we consider two multi-objective evolutionary algorithms, i.e., a multi-objective particle swarm optimization algorithm (MOPSO) and a fast non-dominated sorting genetic algorithm (NSGA-III) and solve a single machine scheduling problem. We demonstrate the applicability and efficiency of the proposed hybrid approach in prov
Computational science is placing new demands on distributed computing systems as the rate of data acquisition is far outpacing the improvements in processor speed. evolutionary algorithms provide efficient means of op...
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ISBN:
(纸本)9783642136443
Computational science is placing new demands on distributed computing systems as the rate of data acquisition is far outpacing the improvements in processor speed. evolutionary algorithms provide efficient means of optimizing the increasingly complex models required by different scientific projects, which can have very complex search spaces with many local minima. This work describes different validation strategies used by [email protected], a volunteer computing project created to address the extreme computational demands of 3-dimensionally modeling the Milky Way galaxy, which currently consists of over 27,000 highly heterogeneous and volatile computing hosts, which provide a combined computing power of over 1.55 petaflops. The validation strategies presented form a foundation for efficiently validating evolutionary algorithms on unreliable or even partially malicious computing systems, and have significantly reduced the time taken to obtain good fits of [email protected]'s astronomical models.
Memetic algorithms are popular hybrid search heuristics that integrate local search into the search process of an evolutionary algorithm in order to combine the advantages of rapid exploitation and global optimisation...
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We analyse the performance of well-known evolutionary algorithms (1+1) EA and (1+λ) EA in the prior noise model, where in each fitness evaluation the search point is altered before evaluation with probability p. We p...
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Collective movements are pervasive behaviours among social organisms and have led to the development of many models. However, modelling animal trajectories and social interactions in simple bounded environments remain...
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The average convergence rate (ACR) measures how fast the approximation error of an evolutionary algorithm converges to zero per generation. It is defined as the geometric average of the reduction rate of the approxima...
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In the empirical study of evolutionary algorithms, the solution quality is evaluated by either the fitness value or approximation error. The latter measures the fitness difference between an approximation solution and...
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