This paper describes an evolutionary Algorithm that repairs to solve Constraint Satisfaction Problems. Knowledge about properties of the constraints network can permit to define a fitness function which is used to imp...
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This paper presents a simple approach used to speed up the convergence of an evolutionary optimizer. It uses Regularized Radial Basis Functions Networks in order to model the objective-function and to provide an inexp...
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In recent years, the design of selection mechanisms based on performance indicators has become a very popular trend in the development of new Multi-Objective evolutionary algorithms (MOEAs). The main motivation has be...
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It is not usual practice in the evolutionary algorithms area to benchmark different operations in order to choose the best language for a single or multilanguage implementation. Researchers rely instead on common prac...
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The paper present an application of evolutionary algorithms in environmental sciences. It describes the Anaerobic Digestion Process and a suitable mathematical model (Anaerobic Digestion Model No.1). The Anaerobic Dig...
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This paper is a first step to formal comparisons of several leading optimization algorithms, establishing guidance to practitioners for when to use or not use a particular method. The focus in this paper is four gener...
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In this paper, some guidelines for setting the parameters of quantum-inspired evolutionary algorithm (QEA) are presented. QEA is based on the concept and principles of quantum computing, such as a quantum bit and supe...
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作者：
Chandra, ArjunYao, Xin
School of Computer Science The University of Birmingham Edgbaston Birmingham B15 2TT United Kingdom
Enforcing diversity explicitly in ensembles while at the same time making individual predictors accurate as well has been shown to be promising. This idea was recently taken into account in the algorithm DIVACE. There...
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ISBN:
(纸本)2930307056
Enforcing diversity explicitly in ensembles while at the same time making individual predictors accurate as well has been shown to be promising. This idea was recently taken into account in the algorithm DIVACE. There have been a multitude of theories on how one can enforce diversity within a combined predictor setup. This paper aims to bring these theories together in an attempt to synthesise a framework that can be used to engender new evolutionary ensemble learning algorithms. The framework treats diversity and accuracy as evolutionary pressures that can be exerted at multiple levels of abstraction and is shown to be effective.
In this paper we discuss the possibility of novel mutual fusion of evolutionary algorithms, complex networks, strange dynamics and hidden attractors. As demonstrated in previous research papers, evolutionary algorithm...
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ISBN:
(纸本)9781631901362
In this paper we discuss the possibility of novel mutual fusion of evolutionary algorithms, complex networks, strange dynamics and hidden attractors. As demonstrated in previous research papers, evolutionary algorithms are capable of very complex tasks such as chaotic system control, identification or synthesis and vice versa, chaos can be observed also in the evolutionary dynamics. We pro-pose a novel approach non how to analyze and control dynamic of evolutionary algorithm and also discuss possibility on strange dynamics analysis that is a part of dynamic of evolutionary algorithms. In any words, we propose to understand algorithms as a discrete dynamical system that exhibit wide spectra behavior that can be controlled and analyzed.
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