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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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Boiler Flow Control using Optimal Fuzzy Supervisory PID Controller 5

Boiler Flow Control using Optimal Fuzzy Supervisory PID Cont...

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5th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Bendib, R. Batout, N. Univ 20 Aout 1955 Skikda Dept Petrochem & Proc Engn Skikda Algeria Lab Signals & Syst IGEE Boumerdes Algeria

ISBN: (纸本)9781538650653

Steam generation systems are a crucial part of most power plants. Therefore, boiler control is an important problem for power plants that are frequently changing load or subject to sudden load disturbances, which are common in current market driven electricity industry. In this paper, the steam flow parameters of a boiler are controlled using fuzzy supervisory PID controller and then optimized using hierarchical genetic algorithm to find the best values of proportional gain (Kp), integral gain (KI), derivative gain (KD).

关键词： boiler fuzzy supervisory hierarchical genetic algorithm PID genetic fuzzy controls

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Research on hierarchical genetic algorithm Optimized Based on Fuzzy Neural Network 1

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International Conference on Applied Informatics and Communication (ICAIC 2011)

作者： Hao, Yuan Ren, Zhaohui Wang, Bingcheng Northeastern Univ Sch Mech Engn & Automat Shenyang 110004 Liaoning Provin Peoples R China Shenzhen Univ Sch Mech Engn Shenzhen 518040 Peoples R China

ISBN: (数字)9783642232206

ISBN: (纸本)9783642232190

A new algorithm of fuzzy neural network learning is presented. It is based on combining genetic algorithm of hierarchical structure with evolution programming. This algorithm is used to optimize the structure and parameters of fuzzy neural network, reject redundant nodes and redundancy connections, and improve the treatment ability of the network. The results of analysis and experiment show that, by using this method the fuzzy neural network of mechanical fault diagnosis has good concise structure and diagnosis effect.

关键词： Fuzzy neural network hierarchical genetic algorithm Evolutionary programming

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Greenfield distribution network expansion strategy with hierarchical GA and MCDEA under uncertainty

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年 79卷 245-252页

作者： Verma, Mandhir Kumar Mukherjee, V. Yadav, Vinod Kumar Indian Sch Mines Dept Elect Engn Dhanbad Jharkhand India Galgotias Univ Dept Elect Engn Gb Nagar Uttar Pradesh India

Distribution network expansion planning (DNEP) is becoming more complex in nature. Addition of new load centers, due to increasing conversion of greenfield areas into habitats, have generated need of more intense and highly structured planning strategies. Micro level work on distribution expansion planning has been ignored by most of the researchers mainly in Indian scenario. Since practical distribution networks are quite large, number of candidates (load centers) will be more and, hence, number of variables (electrical parameters and new load center feasible connections with the existing system) are remarkable. Optimizing a large system may result in significant decrease of accuracy and increase of computation time. For deciphering this issue, segmentation procedure has been applicable. For this purpose, a sensitivity analysis has been applied to find dependent variables. It is obvious that a correct segmentation can decrease computation time (as a single task is operated in segments simultaneously) while accuracy decreases negligibly. In present work, a scheme has been introduced to connect three greenfield load centers with existing primary distribution system by using hierarchical genetic algorithm (HGA). HGA is an integrated approach of analytical hierarchical process and genetic algorithm. The paper reports best selection of investment with finest voltage profile and least losses while maintaining radiality of the system. DNEP has been done at micro level and proposed methodology has been tested on a small dimension practical distribution system. The novelty of this paper is to optimize the best possible selection of connection of new load centers with existing system with the help of AHP and GA and results have been verified with advance optimal tool multiple criteria data envelopment analysis (MCDEA). HGA and MCDEA are applied to practical nine bus distribution system and the results are presented and compared. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Distribution network expansion planning Analytical hierarchy process hierarchical genetic algorithm Load flow analysis Multiple criteria data envelopment analysis Decision making units

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Optimization of modular granular neural networks using hierarchical genetic algorithms for human recognition using the ear biometric measure

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年 27卷 41-56页

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

A new model of a modular neural network (MNN) using a granular approach and its optimization with hierarchical genetic algorithms is proposed in this paper. This model can be used in different areas of application, such as human recognition and time series prediction. In this paper, the proposed model is tested for human recognition based on the ear biometric measure. A benchmark database of the ear biometric measure is used to illustrate the advantages of the proposed model over existing approaches in the literature. The proposed method consists in the optimization of the design parameters of a modular neural network, such as number of modules, percentage of data for the training phase, goal error, learning algorithm, number of hidden layers and their respective number of neurons. This method also finds out the amount of and the specific data that can be used for the training phase based on the complexity of the problem. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Modular neural networks Granular computing hierarchical genetic algorithm Human recognition

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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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Management planning of fast-growing plantations based on a bi-level programming model

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FOREST POLICY AND ECONOMICS 2014年 38卷 173-177页

作者： Zhai, Wenyuan Zhao, Yingzi Lian, Xiangru Yang, Mingming Lu, Fadian Shandong Agr Univ Coll Forestry Tai An 271018 Shandong Peoples R China

The management of fast-growing plantations involves two-layer decision makers, such as forestry management departments and direct operators. Direct operators are interested in the economic benefits of plantation management, while forestry management departments aim to preserve reasonable age structures and sustainable development within plantations. A hi-level programming model should be suitable for optimizing the management of fast-growing plantations. Based on the current management situation in China, which is characterized by decentralized management on a small scale and a lack of integrated planning, we established a nonlinear bi-level programming model to improve the management of fast-growing plantations through the comprehensive consideration of upper- and lower-level targets. An effective hierarchical genetic algorithm was applied to the model, and the specific steps in the solution process were reported. Finally, the scientific value of the model and the availability of the algorithm are confirmed using an example of a fast-growing plantation. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Fast-growing plantation Management planning Bi-level programming model hierarchical genetic algorithm

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Adaptive fuzzy identification and predictive control for industrial processes

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第17期40卷 6964-6975页

作者： Mendes, Jerome Araujo, Rui Souza, Francisco Univ Coimbra Dept Elect & Comp Engn DEEC UC Inst Syst & Robot ISR UC PT-3030290 Coimbra Portugal

This paper proposes a method for adaptive identification and control for industrial applications. The learning of a T-S fuzzy model is performed from input/output data to approximate unknown nonlinear processes by a hierarchical genetic algorithm (HGA). The HGA approach is composed by five hierarchical levels where the following parameters of the T-S fuzzy system are learned: input variables and their respective time delays, antecedent fuzzy sets, consequent parameters, and fuzzy rules. In order to reduce the computational cost and increase the algorithm's performance an initialization method is applied on HGA. To deal with nonlinear plants and time-varying processes, the T-S fuzzy model is adapted online to maintain the quality of the identification/control. The identification methodology is proposed for two application problems: (1) the design of data-driven soft sensors, and (2) the learning of a model for the Generalized predictive control (GPC) algorithm. The integration of the proposed adaptive identification method with the GPC results in an effective adaptive predictive fuzzy control methodology. To validate and demonstrate the performance and effectiveness of the proposed methodologies, they are applied on identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment system;and on control of a simulated continuous stirred tank reactor (CSTR) and on a real experimental setup composed of two coupled DC motors. The results are presented, showing that the developed evolving T-S fuzzy model can identify the nonlinear systems satisfactorily and it can be used successfully as a prediction model of the process for the GPC controller. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Fuzzy identification hierarchical genetic algorithm Predictive fuzzy control Fuzzy generalized predictive control

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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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Optimal design of modular configuration considering hierarchicy of a product function structure

Optimal design of modular configuration considering hierarch...

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12th International Design Conference, DESIGN 2012

作者： Kobayashi, M. Matsumoto, Y. Higashi, M. Toyota Technological Institute 2-12-1 Hisakata Tempaku Nagoya 468-8511 Japan

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