To gain a better theoretical understanding of how evolutionary algorithms (EAs) cope with plateaus of constant fitness, we propose the n-dimensional PLATEAUk function as natural benchmark and analyze how different var...
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We present an online parallel portfolio selection algorithm based on the island model commonly used for par-allelization of evolutionary algorithms. In our case each of the islands runs a different optimization algori...
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Many chaotic dynamical systems can produce time series with a wide range of temporal and spectral properties as a function of only a few fixed parameters. This malleability invites their use as tools for shaping or de...
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Many chaotic dynamical systems can produce time series with a wide range of temporal and spectral properties as a function of only a few fixed parameters. This malleability invites their use as tools for shaping or designing inputs used to drive a separate dynamical system of interest. By specifying an objective function and employing an evolutionary algorithm to manipulate the parameters governing the dynamics of the forcing system, the output of the driven system is made to approach an optimal response subject to desired constraints. The technique's versatility is demonstrated for two different applications: damage detection in structures and phase-locked loop disruption.
The issue of controlling values of various parameters of an evolutionary algorithm is one of the most important and interesting areas of research in evolutionary computation. In this paper we propose two new parameter...
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The issue of controlling values of various parameters of an evolutionary algorithm is one of the most important and interesting areas of research in evolutionary computation. In this paper we propose two new parameter control strategies for evolutionary algorithms based on the ideas of reinforcement learning. These strategies provide efficient and low-cost adaptive techniques for parameter control and they preserve the original design of the evolutionary algorithm, as they can be included without changing either the structure of the algorithm nor its operators design. (C) 2010 Elsevier Inc. All rights reserved.
Nowadays, with the massive integration of distributed renewable generation, electric vehicles ... to the distribution level of the electricity grid, the traditional strategy for monitoring distribution systems is no m...
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ISBN:
(纸本)9781728111834;9781728111827
Nowadays, with the massive integration of distributed renewable generation, electric vehicles ... to the distribution level of the electricity grid, the traditional strategy for monitoring distribution systems is no more valid. In fact, Distribution Networks should become observable and controlled in real time in the same way as the transmission systems. Therefore, Distribution System State Estimation (DSSE) represents a relevant research topic to embrace the new conjuncture of Smart Grid, new techniques should be developed to ensure the observability and manage the bidirectional power flows generated by renewable resources. This paper provides a detailed survey of DSSE techniques available in literature: DSSE techniques based on adapting Weighted Least Squares Algorithm from transmission to distribution network according to the different state variables developed (Node voltage, Branch current, Branch power ....) and DSSE methods based on evolutionary algorithms (Artificial Neural Network, Fuzzy Logic, Particle Swarm Optimization...) are presented. The advantages and disadvantages of each method are discussed finally, directions for future research are suggested.
This paper compares the effectiveness of five state-of-the-art multiobjective evolutionary algorithms (MOEAs) together with a steady state evolutionary algorithm on the mean-variance cardinality constrained portfolio ...
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This paper compares the effectiveness of five state-of-the-art multiobjective evolutionary algorithms (MOEAs) together with a steady state evolutionary algorithm on the mean-variance cardinality constrained portfolio optimization problem (MVCCPO). The main computational challenges of the model are due to the presence of a nonlinear objective function and the discrete constraints. The MOEAs considered are the Niched Pareto genetic algorithm 2 (NPGA2), non-dominated sorting genetic algorithm II (NSGA-II), Pareto envelope-based selection algorithm (PESA), strength Pareto evolutionary algorithm 2 (SPEA2). and e-multiobjective evolutionary algorithm (e-MOEA). The computational comparison was performed using formal metrics proposed by the evolutionary multiobjective optimization community on publicly available data sets which contain up to 2196 assets. (C) 2011 Elsevier Ltd. All rights reserved.
Because of increasing transport and trade there is a growing threat of marine invasive species being introduced into regions where they do not presently occur. So that the impacts of such species can be mitigated, it ...
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Because of increasing transport and trade there is a growing threat of marine invasive species being introduced into regions where they do not presently occur. So that the impacts of such species can be mitigated, it is important to predict how individuals, particularly passive dispersers are transported and dispersed in the ocean as well as in coastal regions so that new incursions of potential invasive species are rapidly detected and origins identified. Such predictions also support strategic monitoring, containment and/or eradication programs. To determine factors influencing a passive disperser, around coastal New Zealand, data from the genus Physalia (Cnidaria: Siphonophora) were used. Oceanographic data on wave height and wind direction and records of occurrences of Physalia on swimming beaches throughout the summer season were used to create models using artificial neural networks (ANNs) and Naive Bayesian Classifier (NBC). First, however, redundant and irrelevant data were removed using feature selection of a subset of variables. Two methods for feature selection were compared, one based on the multilayer perceptron and another based on an evolutionary algorithm. The models indicated that New Zealand appears to have two independent systems driven by currents and oceanographic variables that are responsible for the redistribution of Physalia from north of New Zealand and from the Tasman Sea to their subsequent presence in coastal waters. One system is centred in the east coast of northern New Zealand and the other involves a dynamic system that encompasses four other regions on both coasts of the country. Interestingly, the models confirm, molecular data obtained from Physalia in a previous study that identified a similar distribution of systems around New Zealand coastal waters. Additionally, this study demonstrates that the modelling methods used could generate valid hypotheses from noisy and complicated data in a system about which there is little previou
Imbalanced data with a skewed class distribution are common in many real-world applications. Deep Belief Network (DBN) is a machine learning technique that is effective in classification tasks. However, conventional D...
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This paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the mutation distribution in evolutionary algorithms. The shape of the q-Gaussian mutation distribution is controlled by a re...
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This paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the mutation distribution in evolutionary algorithms. The shape of the q-Gaussian mutation distribution is controlled by a real parameter q. In the proposed method, the real parameter q of the q-Gaussian mutation is encoded in the chromosome of individuals and hence is allowed to evolve during the evolutionary process. In order to test the new mutation operator, evolution strategy and evolutionary programming algorithms with self-adapted q-Gaussian mutation generated from anisotropic and isotropic distributions are presented. The theoretical analysis of the q-Gaussian mutation is also provided. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutations in the optimization of a set of test functions. Experimental results show the efficiency of the proposed method of self-adapting the mutation distribution in evolutionary algorithms.
This paper investigates the issue of tuning the Proportional Integral and Derivative (PID) controller parameters for a greenhouse climate control system using an evolutionary Algorithm (EA) based on multiple performan...
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This paper investigates the issue of tuning the Proportional Integral and Derivative (PID) controller parameters for a greenhouse climate control system using an evolutionary Algorithm (EA) based on multiple performance measures such as good static-dynamic performance specifications and the smooth process of control. A model of nonlinear thermodynamic laws between numerous system variables affecting the greenhouse climate is formulated. The proposed tuning scheme is tested for greenhouse climate control by minimizing the integrated time square error (ITSE) and the control increment or rate in a simulation experiment. The results show that by tuning the gain parameters the controllers can achieve good control performance through step responses such as small overshoot, fast settling time, and less rise time and steady state error. Besides, it can be applied to tuning the system with different properties, such as strong interactions among variables, nonlinearities and conflicting performance criteria. The results implicate that it is a quite effective and promising tuning method using multi-objective optimization algorithms in the complex greenhouse production.
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