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hierarchical genetic algorithm with new evaluation function and bi-coded representation for the selection of features considering their confidence rate

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APPLIED SOFT COMPUTING 2011年第2期11卷 2501-2509页

作者： Hamdani, Tarek M. Won, Jin-Myung Alimi, Adel M. Karray, Fakhri Univ Sfax Natl Sch Engineers ENIS Res Grp Intelligent Machines REGIM Sfax 3038 Tunisia Univ Waterloo Dept Elect & Comp Engn Pattern Anal & Machine Intelligence Res Grp Waterloo ON N2L 3G1 Canada

In this paper, we propose a new feature selection method based on a hierarchical genetic algorithm (GA) with a new evaluation function and a bi-coded representation. The hierarchical GA with homogeneous and heterogeneous population is used to minimize the computational load and to accelerate convergence speed. The fitness function is designed to find the solution that both maximizes the recognition rate and minimizes the feature set size. Each solution candidate is represented by two chromosomes which lengths are identical to the number of available features. The first binary chromosome represents the presence of features in the solution candidate;the second represents the confidence rates of features, which are used to assign different weights to features during the classification procedure and to achieve more accurate classifier. The proposed method is tested using five databases and is shown to outperform many commonly used feature selection algorithms. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Pattern recognition Features selection hierarchical genetic algorithm Evaluation function Bi-coded chromosomes Confidence rate

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A hierarchical genetic algorithm for reliability based design of geometrically non-linear composite structures

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COMPOSITE STRUCTURES 2001年第1期54卷 37-47页

作者： António, CAC Univ Porto Fac Engn P-4200465 Oporto Portugal

A design optimisation methodology based on structural reliability of beam reinforced composite shell structures with non-linear geometric behaviour is proposed. The formulation involves probabilistic stress, displacement and buckling constraints. The structural integrity is evaluated through the reliability index using a Lind-Hasofer second-order-second-moment approximation method together with the Newton-Raphson iterative procedure and the arc-length method. The random variables are the mechanical properties of the laminates treated as homogeneous orthotropic materials. A new methodology based on an evolutionary strategy searching the global most probable failure point (MPP) for composite structures under non-linear geometric behaviour is proposed. The optimal design performs on a hierarchical genetic algorithm (HGA) based on weight minimisation under prescribed reliability constraints. The design variables are the ply angle, the ply thickness and the geometric variables associated with the cross sections of the stiffeners. To demonstrate the applicability of the proposed developments, optimisation problems are presented. (C) 2001 Elsevier Science Ltd. All rights reserved.

关键词： reliability geometrically non-linear composite structures hierarchical genetic algorithm

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Simultaneous optimization of photostrictive actuator locations, numbers and light intensities for structural shape control using hierarchical genetic algorithm

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ADVANCES IN ENGINEERING SOFTWARE 2015年 88卷 21-29页

作者： Zhao, Yu Zheng, Shijie Wang, Hongtao Yang, Liuqing Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Mech Struct Nanjing 210016 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Jiangsu Peoples R China

The present paper introduces an investigation into simultaneous optimization of the PbLaZrTi-based actuator configuration and corresponding applied light intensity for morphing beam structural shapes. A finite element formulation for multiphysics analysis of coupled opto-electro-thermo-mechanical fields in PbLaZrTi ceramics is derived and verified with the theoretical solution and the commercial software ANSYS. This element is then used to simulate beam bending shape control using the orthotropic PbLaZrTi actuators and the simultaneous optimization. In this procedure, the controlling and geometrical variables are simultaneously optimized via a hierarchical genetic algorithm. A bi-coded chromosome is proposed in a hierarchical mode, which consists of some control genes (i.e. actuator location and number) and parametric genes (i.e. applied light intensity). Whether the parametric gene is activated or not is managed by the value of the first-grade control genes. The numerical results demonstrate that the achieved beam bending shapes correlate remarkably well with the expected ones and the simultaneous optimization of photostrictive actuator locations, numbers and light intensities can result in optimal actuator layout with less PbLaZrTi actuators and irradiated light energy. The simulation results also show that the hierarchical genetic algorithm has more superior performance over the conventional real-coded genetic algorithm. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Simultaneous optimization Wireless shape control Finite element Photostrictive actuators hierarchical genetic algorithm Coupled opto-electro-thermo-mechanical fields

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A study on synergy of multiple crossover operators in a hierarchical genetic algorithm applied to structural optimisation

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2009年第2期38卷 117-135页

作者： Antonio, Carlos Conceicao Univ Porto Fac Engn P-4200465 Oporto Portugal

Development of crossover operators is based on three different mechanisms: mating selection mechanism, offspring generation mechanism and offspring selection mechanism. Most crossover operators are able to get exploration or exploitation of the domain depending on the way they handle the current diversity of the population. Each crossover operator directs the search towards a different region in the neighbourhood of the parents. The quality of the elements belonging to the visited region depends on the particular problem to be solved. This is confirmed by the well known No Free Lunch (NFL) theorems. The simultaneous use of diverse crossover operators on the population may induce more efficient algorithms. The aim of this paper is to analyse and to study complementary properties resulting from synergy effects using several crossover operators in particular for a hierarchical genetic algorithm. The reached improvements using multiple crossover operators will be analysed through some standard optimisation examples of hybrid composite structures.

关键词： hierarchical genetic algorithm Multiple crossovers Synergy effects Structural optimisation

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Automatic extraction of the fuzzy control system by a hierarchical genetic algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年 29卷 70-78页

作者： Mendes, Jerome Araujo, Rui Matias, Tiago Seco, Ricardo Belchior, Carlos Univ Coimbra Inst Syst & Robot ISR UC P-3030290 Coimbra Portugal Univ Coimbra Dept Elect & Comp Engn DEEC UC P-3030290 Coimbra Portugal

The paper proposes a new method to automatically extract all fuzzy parameters of a Fuzzy Logic Controller (FLC) in order to control nonlinear industrial processes. The main objective of this paper is the extraction of a FLC from data extracted from a given process while it is being manually controlled. The learning of the FLC is performed by a hierarchical genetic algorithm (HGA), from a set of process-controlled input/output data. The algorithm is composed by a five level structure, being the first level responsible for the selection of an adequate set of input variables. The second level considers the encoding of the membership functions. The individual rules are defined on the third level. The set of rules are obtained on the fourth level, and finally, the fifth level selects the elements of the previous levels, as well as, the t-norm operator, inference engine and defuzzifier methods which constitute the FLC. To optimize the proposed method, the HGA's initial populations are obtained by an initialization algorithm. This algorithm has the main goal of providing a good initial solution for membership functions and rule based populations, enhancing the GA's tuning. Moreover, the HGA is applied to control the dissolved oxygen in an activated sludge reactor within a wastewater treatment plant. The results are presented, showing that the proposed method extracted all the parameters of the fuzzy controller, successfully controlling a nonlinear plant. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Fuzzy control hierarchical genetic algorithm Activated sludge process Fuzzy knowledge base

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Multilevel Redundancy Allocation Optimization Using hierarchical genetic algorithm

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IEEE TRANSACTIONS ON RELIABILITY 2008年第4期57卷 650-661页

作者： Kumar, Ranjan Izui, Kazuhiro Masataka, Yoshimura Nishiwaki, Shinji Kyoto Univ Dept Aeronaut & Astronaut Kyoto 6068325 Japan

This paper proposes a generalized formulation for multilevel redundancy allocation problems that can handle redundancies for each unit in a hierarchical reliability system, with structures containing multiple layers of subsystems and components. Multilevel redundancy allocation is an especially powerful approach for improving the system reliability of such hierarchical configurations, and system optimization problems that take advantage of this approach are termed multilevel redundancy allocation optimization problems (MRAOP). Despite the growing interest in MRAOP, a survey of the literature indicates that most redundancy allocation schemes are mainly confined to a single level, and few problem-specific MRAOP have been proposed or solved. The design variables in MRAOP are hierarchically structured. This paper proposes a new variable coding method in which these hierarchical design variables are represented by two types of hierarchical genotype, termed ordinal node, and terminal node. These genotypes preserve the logical linkage among the hierarchical variables, and allow every possible combination of redundancy during the optimization process. Furthermore, this paper developed a hierarchical genetic algorithm (HGA) that uses special genetic operators to handle the hierarchical genotype representation of hierarchical design variables. For comparison, the customized HGA, and a conventional genetic algorithm (GA) in which design variables are coded in vector forms, are applied to solve MRAOP for series systems having two different configurations. The solutions obtained when using HGA are shown to be superior to the conventional GA solutions, indicating that the HGA here is especially suitable for solving MRAOP for series systems.

关键词： hierarchical genetic algorithm hierarchical genotype multilevel redundancy allocation reliability optimization

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Optimization of type-1, interval type-2 and general type-2 fuzzy inference systems using a hierarchical genetic algorithm for modular granular neural networks

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GRANULAR COMPUTING 2019年第2期4卷 211-236页

作者： Melin, Patricia Sanchez, Daniela Tijuana Inst Technol Tijuana Mexico

In this paper, a new method for fuzzy inference system optimization is presented. The proposed method performs type-1, interval type-2 and general type-2 fuzzy inference systems design using a hierarchical genetic algorithm as optimization method. This method is an improvement of a fuzzy system optimization approach presented in previous works where only the optimization of type-1 and interval type-2 fuzzy inference systems was performed using a human recognition application. The human recognition is performed using three biometric measures namely iris, ear, and voice, where the main idea is to perform the combination of responses in the modular neural networks using an optimized fuzzy inference system to improve the final results without and with noisy conditions. The main contribution of the proposed method is an optimization able to select type of fuzzy logic, granulation of each fuzzy variable and fuzzy rules selection to design optimal fuzzy inference systems applied in combining modular neural networks responses. The results obtained show the effectiveness of the proposed method to design structures of fuzzy inference systems. Statistical comparisons are performed with previous results, where better results can be observed using the proposed method. The design of optimal structures of fuzzy inference systems include among other parameters;type of fuzzy logic (type-1, interval and general type-2 fuzzy logic), type of inference model (Mamdani model or Sugeno model), and consequents of the fuzzy if-then rules.

关键词： Modular neural networks Type-1 fuzzy logic Interval type-2 fuzzy logic General type-2 fuzzy logic Human recognition hierarchical genetic algorithm

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Optimizing a fuzzy neural network with a hierarchical genetic algorithm

Optimizing a fuzzy neural network with a Hierarchical Geneti...

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6th International Conference on Machine Learning and Cybernetics

作者： Ma, Ming Zhang, Li-Biao Beihua Univ Informat Manage Ctr Jilin 132013 Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130122 Peoples R China

ISBN: (纸本)9781424409723

We have proposed an algorithm to optimize fuzzy neural network based on hierarchical genetic algorithm. It can evolve both the fuzzy neural network's topology and weighting parameters. In a real problem, it can automatically obtain the near-optimal structure of fuzzy neural network according to the requirements. Numerical simulations show the effectiveness of the proposed algorithm.

关键词： hierarchical genetic algorithm fuzzy neural network optimize

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An Optimized Power Terminal Communication Access Network Planning Based on hierarchical genetic algorithm 9

An Optimized Power Terminal Communication Access Network Pla...

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2017 9th IEEE International Conference on Communication Software and Networks (ICCSN 2017)

作者： Fanbo Meng Honghao Zhao Bingyan Yu Qian Liu Qiang Cheng Jie Chen Jian Li State Grid LIAONING Electronic Power Supply Corporation Institute of Technology and Standards Research China Academy of Information and Communications Technology

ISBN: (纸本)9781509038237;9781509038220

Power terminal communication access network(PTCAN) is an important network for smart grid applications due to its ability of monitoring and remotely controlling smart power distribution devices and carrying the consumer information. In this paper, a hierarchical genetic algorithm is proposed to plan and optimize the Industrial Ethernet based PTCAN. The key idea of the algorithm is to transform the treeform topology of power distribution network to a ring-form topology of PTCAN. A depth-first pre-order traversal based chromosome initialization method is raised in this paper and a hierarchical planning scheme is proposed to simplified the algorithm complexity. Corresponding evaluation and selection function, crossover and mutation method are designed and described in detail. At last, a simulation is performed, the result shows our algorithm is fully feasible and has advantages of faster convergence rate and better performance.

关键词： power terminal communication access network Industrial Ethernet hierarchical genetic algorithm depth-first pre-order traversal

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Feature Selection of EEG Oscillatory Activity Related to Motor Imagery Using a hierarchical genetic algorithm

Feature Selection of EEG Oscillatory Activity Related to Mot...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Leon, Miguel Ballesteros, Joaquin Tidare, Jonatan Xiong, Ning Astrand, Elaine Malardalen Univ Sch Innovat Design & Engn Vasteras Sweden

ISBN: (纸本)9781728121536

Motor Imagery (MI) classification from neural activity is thought to represent valuable information that can be provided as real-time feedback during rehabilitation after for example a stroke. Previous studies have suggested that MI induces partly subject-specific EEG activation patterns, suggesting that individualized classification models should be created. However, due to fatigue of the user, only a limited number of samples can be recorded and, for EEG recordings, each sample is often composed of a large number of features. This combination leads to an undesirable input data set for classification. In order to overcome this constraint, we propose a new methodology to create and select features from the EEG signal in two steps. First, the input data is divided into different windows to reduce the cardinality of the input. Secondly, a hierarchical genetic algorithm is used to select relevant features using a novel fitness function which combines the data reduction with a correlation feature selection measure. The methodology has been tested on EEG oscillatory activity recorded from 6 healthy volunteers while they performed an MI task. Results have successfully proven that a classification above 75% can be obtained in a restrictive amount of time (0.02 s), reducing the number of features by almost 90%.

关键词： Motor Imagery EEG Signal hierarchical genetic algorithm

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