Diplomová práce se zabývá použitím zvolených evolučních algoritmů k určení a úpravě parametrů neuronové sítě. K úpravě parametrů sítě se zpětný...
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Diplomová práce se zabývá použitím zvolených evolučních algoritmů k určení a úpravě parametrů neuronové sítě. K úpravě parametrů sítě se zpětným šířením chyby byly použity genetické algoritmy, evoluční strategie a evoluční programování. Součástí práce je program vytvořený v prostředí Matlab, ve kterém byly použité metody testovány na úlohách rozpoznávání vzorů a predikci průběhu funkce. Výsledkem práce jsou grafy průběhu chyby sítě a fitness během úpravy pomocí zvolených algoritmů a průběhů chyby při následném učení.
Obrazové filtry představují část vědní disciplíny zabívající se digitálním zpracováním obrazu. Filtrace obrazu slouží ke zvýraznění určit...
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Obrazové filtry představují část vědní disciplíny zabívající se digitálním zpracováním obrazu. Filtrace obrazu slouží ke zvýraznění určité informace. Můžeme potlačit šum, vyhladit obraz, zvýraznit kontrast nebo detekovat hrany. Samotný návrh obrazových filtrů představuje časově poměrně náročný proces. Je tedy žádoucí jej v maximální míře zautomatizovat a přenechat činnost předem naprogramovanému systému. Práce se zabývá návrhem komponent pro zmíněný systém. Jedná se o část funkce expertního systému, kterému se na vstupu poskytne množina vstupních komponent, z nichž se za použití principů evolučního programování mohou generovat nové filtry a následně vyhodnocovat jejich spolehlivost pro další využití.
This paper proposes a hybrid multilogistic methodology, named logistic regression using initial and radial basis function (RBF) covariates. The process for obtaining the coefficients is carried out in three steps. Fir...
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This paper proposes a hybrid multilogistic methodology, named logistic regression using initial and radial basis function (RBF) covariates. The process for obtaining the coefficients is carried out in three steps. First, an evolutionary programming (EP) algorithm is applied, in order to produce an RBF neural network (RBFNN) with a reduced number of RBF transformations and the simplest structure possible. Then, the initial attribute space (or, as commonly known as in logistic regression literature, the covariate space) is transformed by adding the nonlinear transformations of the input variables given by the RBFs of the best individual in the final generation. Finally, a maximum likelihood optimization method determines the coefficients associated with a multilogistic regression model built in this augmented covariate space. In this final step, two different multilogistic regression algorithms are applied: one considers all initial and RBF covariates (multilogistic initial-RBF regression) and the other one incrementally constructs the model and applies cross validation, resulting in an automatic covariate selection [simplelogistic initial-RBF regression (SLIRBF)]. Both methods include a regularization parameter, which has been also optimized. The methodology proposed is tested using 18 benchmark classification problems from well-known machine learning problems and two real agronomical problems. The results are compared with the corresponding multilogistic regression methods applied to the initial covariate space, to the RBFNNs obtained by the EP algorithm, and to other probabilistic classifiers, including different RBFNN design methods [e.g., relaxed variable kernel density estimation, support vector machines, a sparse classifier (sparse multinomial logistic regression)] and a procedure similar to SLIRBF but using product unit basis functions. The SLIRBF models are found to be competitive when compared with the corresponding multilogistic regression methods and the R
[1] Finding a strategy that allows economically efficient drinking water production at minimal environmental cost is often a complex task. A systematic trade-off among the costs and benefits of possible strategies is ...
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[1] Finding a strategy that allows economically efficient drinking water production at minimal environmental cost is often a complex task. A systematic trade-off among the costs and benefits of possible strategies is required for determining the optimal production configuration. Such a trade-off involves the handling of interdependent and nonlinear relations for drawdown-related objective categories like damage to wetland vegetation, agricultural yield depression, reduction of river base flow rates, and soil subsidence. We developed a method for multiple-objective optimization of drinking water production by combining Busacker and Gowen's [1961] "minimum cost flow'' procedure for optimal use of the transport network with a genetic algorithm (GA) for optimization of other impacts. The performance of the GA was compared with analytically determined solutions of a series of hypothetical case studies. Pareto-optimality and uniqueness of solutions proved to be effective fitness criteria for identifying trade-off curves with the GA.
Cultural Algorithms are computational self-adaptive models which consist of a population and a belief space. The problem-solving experience of individuals selected from the population space by the acceptance function ...
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Cultural Algorithms are computational self-adaptive models which consist of a population and a belief space. The problem-solving experience of individuals selected from the population space by the acceptance function is generalized and stored in the belief space. This knowledge can then control the evolution of the population component by means of the influence function. Here, we examine the role that different forms of knowledge can play in the self-adaptation process within cultural systems. In particular, we compare various approaches that use normative and situational knowledge in different ways to guide the function optimization process. The results in this study demonstrate that Cultural Algorithms are a naturally useful framework for self-adaptation and that the use of a cultural framework to support self-adaptation in evolutionary programming can produce substantial performance improvements over population-only systems as expressed in terms of (1) systems success ratio, (2) execution CPU time, and (3) convergence (mean best solution) for a given set of 34 function minimization problems. The nature of these improvements and the type of knowledge that is most effective in producing them depend on the problem's functional landscape. In addition, it was found that the same held true for the population-only self-adaptive EP systems. Each level of self-adaptation (component, individual, and population) outperformed the others for problems with particular landscape features.
Conventional training methods for diagonal recurrent neural networks identifier are limited to the first and second derivative methods. In this paper, a novel training algorithm based on evolutionary programming (EP) ...
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Conventional training methods for diagonal recurrent neural networks identifier are limited to the first and second derivative methods. In this paper, a novel training algorithm based on evolutionary programming (EP) and particle swarm optimization (PSO) for evolutionary diagonal recurrent neural network (EDRNN) is proposed. Mean-while, a new select mode is given for improving the premature convergence for PSO. Compared with conventional methods, EDRNN has prominent advantage in identifying nonlinear dynamic systems because the structure and weight of EDRNN can be evolved simultaneously. Experimental results of identifying the classical nonlinear dynamic systems confirm that EDRNN-based method is a promising tool for identifier.
During protein synthesis the genetic code links each codon, a triplet of nucleotides, with the corresponding amino acid. Synonymous codons are those that code for the same amino acid. The difference in the frequency o...
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During protein synthesis the genetic code links each codon, a triplet of nucleotides, with the corresponding amino acid. Synonymous codons are those that code for the same amino acid. The difference in the frequency of occurrence of certain synonymous codons over other synonymous codons is called the codon usage bias (CUB). The Zeng and Charlesworth model is used to estimate the strength of CUB. In their model the evolutionary process is represented by a Markov model, which allows the population size to vary over time. In this paper we propose a new method that incorporates demographic changes into the model. The method is a hybrid of two optimizers, the first is evolutionary programming and the second is a version of the genetic algorithms that uses chromosomes of variable lengths, which allows for expressing more demographic changes than what the simplified model presented by Zeng and Charlesworth does. We conduct several simulations to show why this hybridization is necessary, and also to show the superior performance of this new hybrid. (C) 2021 Elsevier B.V. All rights reserved.
Projection techniques are frequently used as the principal means for the implementation of feature extraction and dimensionality reduction for machine learning applications. A well established and broad class of such ...
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Projection techniques are frequently used as the principal means for the implementation of feature extraction and dimensionality reduction for machine learning applications. A well established and broad class of such projection techniques is the projection pursuit (PP). Its core design parameter is a projection index, which is the driving force in obtaining the transformation function via optimization, and represents in an explicit or implicit way the user's perception of the useful information contained within the datasets. This paper seeks to address the problem related to the design of PP index functions for the linear feature extraction case. We achieve this using an evolutionary search framework, capable of building new indices to fit the properties of the available datasets. The high expressive power of this framework is sustained by a rich set of function primitives. The performance of several PP indices previously proposed by human experts is compared with these automatically generated indices for the task of classification, and results show a decrease in the classification errors.
Gasification of black liquor is an alternative recovery technology. Pulp and paper industries are turning their attention to black liquor gasification as a replacement for recovery boilers. Gasification-combined cycle...
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Gasification of black liquor is an alternative recovery technology. Pulp and paper industries are turning their attention to black liquor gasification as a replacement for recovery boilers. Gasification-combined cycle exergoeconomic analyses have been used to study the economical and exergetical performance of black liquor. Both conventional iterative exergoeconomic optimization and multi objective evolutionary optimization have been applied. The iterative optimization results have been compared with evolutionary programming results in order to evaluate the accuracy. The strength of proposed optimization methods are elucidated in a case study applied to Mazandaran Wood and Paper Industries Co. Exergetic efficiency of total system can reach up to 58.3% while it is restricted to 57.0% when using the evolutionary optimization. However, the product cost achieved by evolutionary optimization is about 1.0% less than that of iterative optimizations.
作者：
K.C. ShilpaC. LakshmiNarayanaBMSCE
Department of Electrical Engineering Science Visvesvaraya Technological University Bangalore 560019 India
The concept of the natural computation for optimal scheduling in high level synthesis, for resource constraint and time constraint scheduling problem in automated integrated circuit synthesis using Integer Linear Prog...
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The concept of the natural computation for optimal scheduling in high level synthesis, for resource constraint and time constraint scheduling problem in automated integrated circuit synthesis using Integer Linear programming (ILP) modeling is presented in this paper. This paper compares three natural computations paradigms: (i) evolution optimizer technique genetic algorithm, (ii) evolutionary programming, and (iii) swarm intelligence based particle swarm optimization. Experimental results indicate that evolution based Genetic Algorithm search is more powerful search compared to evolutionary programming and Particle Swam Optimization.
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