This paper proposes and evaluates a multi-objective evolutionary algorithm for survival analysis. One aim of survival analysis is the extraction of models from data that approximate lifetime/failure time distributions...
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This paper proposes and evaluates a multi-objective evolutionary algorithm for survival analysis. One aim of survival analysis is the extraction of models from data that approximate lifetime/failure time distributions. These models can be used to estimate the time that an event takes to happen to an object. To use of multi-objective evolutionary algorithms for survival analysis has several advantages. They can cope with feature interactions, noisy data, and are capable of optimising several objectives. This is important, as model extraction is a multi-objective problem. It has at least two objectives, which are the extraction of accurate and simple models. Accurate models are required to achieve good predictions. Simple models are important to prevent overfitting, improve the transparency of the models, and to save computational resources. Although there is a plethora of evolutionary approaches to extract models for classification and regression, the presented approach is one of the first applied to survival analysis. The approach is evaluated on several artificial datasets and one medical dataset. It is shown that the approach is capable of producing accurate models, even for problems that violate some of the assumptions made by classical approaches. (c) 2006 Elsevier Ireland Ltd. All rights reserved.
Having a full situational awareness while driving is one of the most important perceptions for safe driving which can be reduced by various factors such as in-vehicle infotainment, distraction, or mental load leading....
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Having a full situational awareness while driving is one of the most important perceptions for safe driving which can be reduced by various factors such as in-vehicle infotainment, distraction, or mental load leading. Machine learning methods are being used to optimize for the identification of these inhibiting factors. To do so, three types of data were used: biographic features, physiological signals and vehicle information of 68 participants are being utilized to identify the normal and loaded behaviors. This research, therefore, concentrates on driving behavior analysis using a new automated hybrid framework for detection of performance degradation of drivers due to distraction. The proposed model contains a hybrid of extreme learning neural network, as an ensemble learning method and evolutionary algorithms, to determine the weights of classifiers, for combining several traditional classifiers. The obtained results showcase that the proposed model yields outstanding performance than the other applied methods.
This paper introduces drift analysis approach in studying the convergence and hitting times of evolutionary algorithms. First the methodology of drift analysis is introduced, which links evolutionary algorithms with M...
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This paper introduces drift analysis approach in studying the convergence and hitting times of evolutionary algorithms. First the methodology of drift analysis is introduced, which links evolutionary algorithms with Markov chains or supermartingales. Then the drift conditions which guarantee the convergence of evolutionary algorithms are described. And next the drift conditions which are used to estimate the hitting times of evolutionary algorithms are presented. Finally an example is given to show how to analyse hitting times of EAs by drift analysis approach.
This paper describes the adaptation of evolutionary algorithms (EAs) to the structural optimization of chemical engineering plants, using rigorous process simulation combined with realistic costing procedures to calcu...
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This paper describes the adaptation of evolutionary algorithms (EAs) to the structural optimization of chemical engineering plants, using rigorous process simulation combined with realistic costing procedures to calculate target function values. To represent chemical engineering plants, a network representation with typed vertices and variable structure will be introduced. For this representation, we introduce a technique on how to create problem specific search operators and apply them in stochastic optimization procedures. The applicability of the approach is demonstrated by a reference example. The design of the algorithms will be oriented at the systematic framework of metric-based evolutionary algorithms (MBEAs). MBEAs are a special class of evolutionary algorithms, fulfilling certain guidelines for the design of search operators, whose benefits have been proven in theory and practice. MBEAs rely upon a suitable definition of a metric on the search space. The definition of a metric for the graph representation will be one of the main issues discussed in this paper. Although this article deals with the problem domain of chemical plant optimization, the algorithmic design can be easily transferred to similar network optimization problems. A useful distance measure for variable dimensionality search spaces is suggested.
In this paper we consider the most important questions, research topics and technical tools used in various branches of evolutionary algorithms. The road map we give is to facilitate the readers' orientation in ev...
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In this paper we consider the most important questions, research topics and technical tools used in various branches of evolutionary algorithms. The road map we give is to facilitate the readers' orientation in evolutionary computation theory. In the meanwhile, this survey provides key references for further study and evidence that the field of evolutionary computation is maturing rapidly, having many important results and even more interesting challenges. (C) 1999 Published by Elsevier Science B.V. All rights reserved.
In this paper, examining some games, we show that classical techniques are not always effective for games with not many stages and players and it can't be claimed that these techniques of solution always obtain th...
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In this paper, examining some games, we show that classical techniques are not always effective for games with not many stages and players and it can't be claimed that these techniques of solution always obtain the optimal and actual Nash equilibrium point. For solving these problems, two evolutionary algorithms are then presented based on the population to solve general dynamic games. The first algorithm is based on the genetic algorithm and we use genetic algorithms to model the players' learning process in several models and evaluate them in terms of their convergence to the Nash Equilibrium. in the second algorithm, a Particle Swarm Intelligence Optimization (PSO) technique is presented to accelerate solutions' convergence. It is claimed that both techniques can find the actual Nash equilibrium point of the game keeping the problem's generality and without imposing any limitation on it and without being caught by the local Nash equilibrium point. The results clearly show the benefits of the proposed approach in terms of both the quality of solutions and efficiency.
In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is k...
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In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front (e. g., multimodality and deception). By investigating these different problem features separately, it is possible to predict the kind of problems to which a certain technique is or is not well suited. However, in contrast to what was suspected beforehand, the experimental results indicate a hierarchy of the algorithms under consideration. Furthermore, the emerging effects are evidence that the suggested test functions provide sufficient complexity to compare multiobjective optimizers. Finally, elitism is shown to be an important factor for improving evolutionary multiobjective search.
There exist several methods for binary classification of gene expression data sets. However, in the majority of published methods, little effort has been made to minimize classifier complexity. In view of the small nu...
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There exist several methods for binary classification of gene expression data sets. However, in the majority of published methods, little effort has been made to minimize classifier complexity. In view of the small number of samples available in most gene expression data sets, there is a strong motivation for minimizing the number of free parameters that must be fitted to the data. In this paper, a method is introduced for evolving (using an evolutionary algorithm) simple classifiers involving a minimal subset of the available genes. The classifiers obtained by this method perform well, reaching 97% correct classification of clinical outcome on training samples from the breast cancer data set published by van't Veer, and up to 89% correct classification on validation samples from the same data set, easily outperforming previously published results.
Objective. Living organisms regulate the expression of genes using complex interactions of transcription factors, messenger RNA and active protein products. Due to their complexity, gene-regulatory networks are not fu...
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evolutionary algorithms are powerful search techniques which have been used successfully in many different domains. Parallel evolutionary algorithm has become a research focus due to its easy implement and promise sub...
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evolutionary algorithms are powerful search techniques which have been used successfully in many different domains. Parallel evolutionary algorithm has become a research focus due to its easy implement and promise substantial gains in performance. In this paper a framework of tree-modelbased parallel evolutionary algorithm (T-PEA) is proposed. The presented method employs Bayesian Dirichlet metric to construct a tree model from a set of potential solutions, which is then used to model potential solutions and guide exploration in the search space. The correctness and rationality of the proposed method for learning tree models are analyzed and proved in the context of genetic and evolutionary. The method is important not only for T-PEA, but also for machine learning and data mining. The experimental results show that the proposed algorithm can efficiently and rapidly converge and obtain the optimal solution from all test functions.
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