This article presents iterative estimation method with usage of evolutionary algorithms of the narrowband propagation channel impulse response, which can be used in reception based on maximum likehood sequence detecti...
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This paper presents a new evolutionary algorithm, called Routes Generation evolutionary Algorithm with Knowledge (RGEAwK), for determining routes with optimal travel time in graph which models the public transport net...
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The paper presents the recent developments in Hierarchical Parallel evolutionary algorithms to speed up optimisation of aerodynamic shapes. One is the implementation of different models in different layers of a Parall...
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The paper presents the recent developments in Hierarchical Parallel evolutionary algorithms to speed up optimisation of aerodynamic shapes. One is the implementation of different models in different layers of a Parallel Genetic Algorithm. The other is Asynchronous Hierarchical Evolution Strategy. These methods are employed to reconstruct a one-dimensional transonic nozzle and a two-dimensional aerofoil shape. Considerable speed up is achieved as a result.
This paper describes a new approach, HIerarchical DEcision Rules (HIDER), for learning generalizable rules in continuous and discrete domains based on evolutionary algorithms. The main contributions of our approach ar...
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This paper describes a new approach, HIerarchical DEcision Rules (HIDER), for learning generalizable rules in continuous and discrete domains based on evolutionary algorithms. The main contributions of our approach are the integration of both binary and real evolutionary coding;the use of specific operators;the relaxing coefficient to construct more flexible classifiers by indicating how general, with respect to the errors, decision rules must be;the coverage factor in the fitness function, which makes possible a quick expansion of the rule size;and the implicit hierarchy when rules are being obtained. HIDER is accuracy-aware since it can control the maximum allowed error for each decision rule. We have tested our system on real data from the UCI Repository. The results of a 10-fold cross-validation are compared to C4.5's and they show a significant improvement with respect to the number of rules and the error rate. (C) 2003 Elsevier Inc. All rights reserved.
Metameric problems are variable-length optimization problems whose representations take on an at least partially segmented structure. This is referred to as a metameric representation. Frequently, each of these segmen...
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Metameric problems are variable-length optimization problems whose representations take on an at least partially segmented structure. This is referred to as a metameric representation. Frequently, each of these segments defines one of a number of analogous components in the solution. Examples include the nodes in a coverage network or turbines in a wind farm. Locating optimal solutions requires, in part, determining the optimal number of components. evolutionary algorithms can be applied but require modifications to the traditional fixed-length operators. This study proposes a new selection operator for metameric problems: length niching selection. First, the population is partitioned into several niches based on solution length. A window function determines at which lengths a niche is formed. Local selection is then applied within each niche independently, resulting in a new parent population formed by a diverse set of solution lengths. A coverage and a wind farm problem are used to demonstrate the effectiveness of the new operator.
Machine learning support for medical decision making is truly helpful only when it meets two conditions: high prediction accuracy and a good explanation of how the diagnosis was reached. Support vector machines (SVMs)...
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Machine learning support for medical decision making is truly helpful only when it meets two conditions: high prediction accuracy and a good explanation of how the diagnosis was reached. Support vector machines (SVMs) successfully achieve the first target due to a kernel-based engine;evolutionary algorithms (EAs) can greatly accomplish the second owing to their adaptable nature. In this context, the current paper puts forward a two-step hybridized methodology, where learning is accurately performed by the SVMs and a comprehensible emulation of the resulting decision model is generated by EAs in the form of propositional rules, while referring only those indicators that highly influence the class separation. An individual highlighting of the medical attributes that trigger a specific diagnosis for a current patient record is additionally obtained;this feature thus increases the confidence of the physician in the resulting automated diagnosis. Without loss of generality, we aim to model three breast cancer instances, for reasons of both high incidence of the disease and the large application of state of the art artificial intelligence methods for this medical task. As such, the prediction of a benign/malignant condition as well as the recurrence/nonrecurrence of a cancer event are studied on the Wisconsin corresponding data sets from the UCI Machine Learning Repository. The proposed hybridization reached its goals. Rule prototypes evolve against a SVM consistent training data, while diversity among the different classes is implicitly preserved. Feature selection eventually leads to a resulting rule set where only the significant medical indicators together with the discriminating threshold values are referred, while individual relevance of attributes can be additionally obtained for each patient. The gain is thus dual: the EA benefits from a noise-free SVM preprocessed data and the resulting SVM model is able to output rules in a comprehensible, concise format for the
The unavailability of excitation measurements poses challenges of application of many structural identification methods due to dealing with two typical types of inverse problems of parameter and force identification s...
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The unavailability of excitation measurements poses challenges of application of many structural identification methods due to dealing with two typical types of inverse problems of parameter and force identification simultaneously. To address this issue, four different identification methods are proposed based on correlation function to identify structures subjected to multiple unknown ambient excitations, namely gradient search, genetic algorithm, particle swarm optimization (PSO), and effective combination of PSO and gradient search. Numerical studies on a cantilever beam and an eight-story frame, experiments verification on the ASCE benchmark frame are carried out to test the performance of proposed methods. In addition, effect of selection of the reference point, number of data points, unknown initial conditions and modelling errors on accuracy of identification results are also investigated. The numerical and experimental results show that the proposed methods are capable of accurately identifying the unknown structural parameters. In particular, the hybrid method of PSO and gradient search, with approach of producing solutions close to the optimal by PSO and then taking as initial values in gradient search to quickly identify structural unknown parameters, achieves the best performance for overall consideration of identification accuracy and computational efficiency.
In present paper an improved multi-objective evolutionary algorithm is used for Pareto optimization of selected coupled problems. Coupling of mechanical, electrical and thermal fields is considered. Boundary-value pro...
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In present paper an improved multi-objective evolutionary algorithm is used for Pareto optimization of selected coupled problems. Coupling of mechanical, electrical and thermal fields is considered. Boundary-value problems of the thermo-elasticity, piezoelectricity and electro-thermo-elasticity are solved by means of finite element method (FEM). Ansys Multiphysics and ***/Marc software are used to solve considered coupled problems. Suitable interfaces between optimization tool and the FEM software are created. Different types of functionals are formulated on the basis of results obtained from the coupled field analysis. Functionals depending on the area or volume of the structure are also proposed. Parametric curves NURBS are used to model some optimized structures. Numerical examples for exemplary three-objective optimization are presented in the paper.
This paper presents a new multiobjective evolutionary algorithm for Environmental/Economic power Dispatch (EED) problem. The EED problem is formulated as a nonlinear constrained multiobjective optimization problem. A ...
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This paper presents a new multiobjective evolutionary algorithm for Environmental/Economic power Dispatch (EED) problem. The EED problem is formulated as a nonlinear constrained multiobjective optimization problem. A new Strength Pareto evolutionary Algorithm (SPEA) based approach is proposed to handle the EED as a true multiobjective optimization problem with competing and noncommensurable objectives. The proposed approach employs a diversity-preserving mechanism to overcome the premature convergence and search bias problems. A hierarchical clustering algorithm is also imposed to provide the decision maker with a representative and manageable Pareto-optimal set. Moreover, fuzzy set theory is employed to extract the best compromise nondominated solution. Several optimization runs of the proposed approach have been carried out on a standard test system. The results demonstrate the capabilities of the proposed approach to generate well-distributed Pareto-optimal solutions of the multiobjective EED problem in one single run. The comparison with the classical techniques demonstrates the superiority of the proposed approach and confirms its potential to solve the multiobjective EED Problem. In addition, the extension of the proposed approach to include more objectives is a straightforward process.
In this work, we present a new approach to solve the location management problem by using the reporting cells strategy. Location management is a very important and complex problem in mobile computing which aims to min...
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In this work, we present a new approach to solve the location management problem by using the reporting cells strategy. Location management is a very important and complex problem in mobile computing which aims to minimize the costs involved. In the reporting cells location management scheme, some cells in the network are designated as reporting cells (RCs). The choice of these cells is not trivial because they affect directly to the cost of the mobile network. This article is focused on the use of high performance computing to execute a parallel heuristic that places optimally the RCs in a mobile network, minimizing its total cost. The main goal of this work is to demonstrate that the collaborative work of different evolutionary algorithms can obtain very good results. For this reason, we have implemented a parallel heuristic and six evolutionary algorithms that works in a parallel way on a cluster to solve the RCs problem.
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