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Unsupervised pattern classification using genetic algorithms

IETE JOURNAL OF RESEARCH 2002年第5期48卷 397-402页

作者： Maulik, U Kalyani Govt Engn Coll Dept Comp Sci & Technol Kalyani 741235 W Bengal India

This article deals with the development of an unsupervised pattern classification technique that exploits the searching capability of genetic algorithms for automatically clustering a given data set into an appropriate number of clusters. Since the number of clusters is not known a priori, a modified string representation, comprising both real numbers and the don't care symbols, is used in order to encode a variable number of clusters. The Dunn's index is used as a measure of the fitness of a chromosome. Effectiveness of the genetic clustering scheme is demonstrated for several artificial and real-life data sets with the number of dimensions ranging from two to nine, and the number of clusters ranging from two to six.

关键词： clustering genetic algorithms Dunn's index real coding

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Intelligent bionic genetic algorithm (IB-GA) and its convergence

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第7期38卷 8804-8811页

作者： Li, Fachao Xu, Li Da Jin, Chenxia Wang, Hong Old Dominion Univ Dept Informat Technol & Decis Sci Norfolk VA 23529 USA Hebei Univ Sci & Technol Sch Econ & Management Shijiazhuang 050018 Hebei Peoples R China Wuhan Univ Technol Inst Syst Sci & Engn Wuhan 430070 Peoples R China N Carolina Agr & Tech State Univ Sch Business & Econ Greensboro NC 27411 USA

As a new kind of intelligence optimization method, genetic algorithms, with the features of simple structure and strong adaptability, achieves great success in many real applications. However, it has many shortcomings such as a greater computation complexity and more chance of being trapped in local states. In this paper, through analyzing the deficiency of the existing genetic operation and the essential characteristics of creature evolution from the angle of improving evolution efficiency, we propose a compound mutation strategy based on mutation criteria function, a multi-reserved strategy based on intelligence evolution, and a weak arithmetic crossover strategy reflecting different evolution modes. Furthermore, we establish an intelligent bionic genetic algorithm with structural features (denoted by IB-GA, for short). Finally, we analyze the performances of IB-GA with the theory of Markov chains and simulation technology. The results indicate that IB-GA is essentially an extension of ordinary GA and obviously better than ordinary GA in terms of computation efficiency and convergence performance. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Genetic algorithm real coding Multi-reserved strategy Weak arithmetic crossover Compound mutation strategy Markov chain

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Tackling real-coded genetic algorithms: Operators and tools for behavioural analysis

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ARTIFICIAL INTELLIGENCE REVIEW 1998年第4期12卷 265-319页

作者： Herrera, F Lozano, M Verdegay, JL Univ Granada Dept Comp Sci & AI ETS Ingn Informat E-18071 Granada Spain

Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations: selection, crossover and mutation. Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet;other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real-coded genetic algorithms. Different models of genetic operators and some mechanisms available for studying the behaviour of this type of genetic algorithms are revised and compared.

关键词： genetic algorithms real coding continuous search spaces

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A long span bridge and a greenhouse roof truss structure optimized by means of a consistent genetic algorithm with a natural crossover

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ENGINEERING COMPUTATIONS 2013年第1期30卷 49-73页

作者： Gutierrez Astudillo, Nayar Cuitlahuac del Rocio Peniche Vera, Rebeca Herrera Ruiz, Gilberto Alvarado Cardenas, Roberto Carrion Viramontes, Francisco J. Univ Autonoma Queretaro Fac Ingn Santiago De Queretaro Mexico Inst Mexican Transporte Lab Desempeno Sanfandila Municipio De Pe Mexico

Purpose - The purpose of this paper is to introduce a novel methodology that has the capability of finding symmetrical and nonsymmetrical solutions in complex design domains without additional tuning when changing the design domain. These go from an academic design domain to a practical one. Design/methodology/approach - Various crossovers operators are applied over the same representation using a genetic algorithm for truss structural optimization cases where literature solutions have a tendency to forced symmetry in order to find an optimal design with fewer iterations. Continuous-discrete representations were cross-bred by a uniform-sbx simultaneous crossover, called zygote crossover. Specialized mutations operations are proposed to generate localized changes to improve the solution according with the design domain. Findings - Design solutions found were lighter and stiffer when comparing against cases reported in current literature and in engineering practice. Also these solutions were found in fewer iterations. Practical implications - The cases solved herein are complex and are a challenge for any optimization routine however practical design limitations are observed in the sense of out plane stability. Further comparisons cases are required in order to generate a less adjusted design, this is because the greenhouse solution had to be stiffened with out of plane bars to give it enough lateral stability. Originality/value - Continuous-discrete representations were cross-bred by a uniform-sbx simultaneous crossover, called natural crossover. Specialized mutations operations are proposed to generate localized changes to improve the solution according with the design domain. This scheme along with a less restrictive environment allows a wider exploration of search space.

关键词： Continuous spatial domain Practical solution real coding Mixed crossover Genetic algorithms Structural design

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Hybrid-coded crossover for binary-coded genetic algorithms in constrained optimization

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ENGINEERING OPTIMIZATION 2004年第1期36卷 101-122页

作者： Wu, WH Lin, CY Natl Taiwan Univ Sci & Technol Dept Mech Engn Taipei 10672 Taiwan

The success of genetic searches greatly depends on appropriate selections for many search parameters. This situation becomes more critical for optimization problems involving sharp constraint surfaces. When penalty function approaches are used to handle the constraints, the genetic algorithm conducts the search on a design space which has continually changing contours during the evolution. This often leads to incorrect gene extinctions at early stages of the search, and thus prevents the search from attaining the final global solution. This paper aims to devise a hybrid-coded crossover strategy for binary-coded genetic algorithms (BGA) which combines the major content of two-point crossover and the full domain representation characteristics embedded in many crossover schemes of real-coded genetic algorithms. It is found that the unique hybrid-coded crossover scheme can largely prevent the binary-coded genetic searches from falsely converging to sub-optimal solutions. Comparisons between BGA with the hybrid-coded crossover and the traditional two-point crossover are conducted in a number of illustrative problems with varied combinations of constraints.

关键词： genetic algorithms binary coding real coding hybrid-coded crossover constrained optimization

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Comprehensive evaluation of integrative drip-irrigation and fertilization using a projection pursuit model based on the improved double chains quantum genetic algorithm

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DESALINATION AND WATER TREATMENT 2017年 66卷 229-234页

作者： Wang, Bai Li, Yanfeng Meng, Yan Liu, Xiao Heilongjiang Water Conservancy Inst Harbin 150080 Peoples R China Inst Food Sci & Engn State Key Lab Simulat & Regulat Water Cycle River Hangzhou Zhejiang Peoples R China Inst Water Resources & Hydropower Res Beijing 100048 Peoples R China

Finding the optimal solution in a single-treatment drip-irrigation and fertilizer scheme is nontrivial because of the inherent difficulty in evaluating the overall benefit of such a scheme. To address this problem, we have developed the project pursuit model based on the improved double chains quantum genetic algorithm, and applied it to integrative drip-irrigation and fertilization. The double chains quantum genetic algorithm was introduced to optimize the projection index function and seek the optimum projection vector. This algorithm was improved by introducing an immunity operator, a simulated annealing operator, and gradually optimizing and compressing the quantum chromosome search space during the evolution process. The improved projection pursuit model was applied to maize cultivation. The results show that the optimization efficiency and global search capability improved substantially. Increased nitrogen splits promoted dry matter above ground and nitrogen uptake in the maize, and also improved the yield and the plumping of seeds. A management practice of 150-200 kg/hm(2) of nitrogen applied at three splits produced the highest production in integrative drip-irrigation and fertilizer schemes for the black soil in Northeast China.

关键词： real coding Quantum computing Quantum genetic algorithm Integrative drip-irrigation Fertilizer

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Multi-reserved strategy and its application in evolutionary computation

Multi-reserved strategy and its application in evolutionary ...

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7th International Conference on Machine Learning and Cybernetics

作者： Li, Fa-Chao Jin, Chen-Xia Hebei Univ Sci & Technol Sch Econ & Management Shijiazhuang 050018 Peoples R China

ISBN: (纸本)9781424420957

As a kind of intelligence computation method, evolutionary computation is widely applied and ceaselessly developed. Generally, it is made up of genetic algorithm, evolutionary strategies and evolutionary programming. And genetic algorithm is one of the most common ones, it has the features of easy structure and strong adaptability, achieves great success in many real fields, but it has much shortcomings such as greater computation complexity, more chance of being trapped into local states and the premature convergence. In (his paper, by analyzing the deficiencies of the existing genetic operation and the essential characteristics of creature evolution, starting from the angle of improving evolution efficiency, we propose multi-reserved strategy based on intelligence evolution;Furthermore, establish a kind of genetic algorithm named by MCA;Finally, we analyze the performances of MCA by the theory of Markov chains and an example. All the results indicate that, MCA is obviously better than ordinary CA in computation efficiency and convergence performance.

关键词： genetic algorithm real coding multi-reserved strategy Markov chain MCA

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A Kind of Genetic Algorithm Based on Compound Mutation Strategy And Performance Study

A Kind of Genetic Algorithm Based on Compound Mutation Strat...

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5th International Conference on Wireless Communications, Networking and Mobile Computing

作者： Li, Fachao Zhang, Tingyu Jin, Chenxia Hebei Univ Sci & Technol Sch Econ & Management Shijiazhuang 050018 Peoples R China Hebei Univ Sci & Technol Sch Sci Shijiazhuang 050018 Peoples R China

ISBN: (纸本)9781424436927

Genetic algorithm (GA), as a kind of important intelligence computing tool, is a wide research content in the academic circle and the application domain now. In this paper, for the mutation operation of GA, by combining with the essential feature, from structural and visualized angle, we implement different mutation strategy to individual of different fitness value in every generation, and establish a genetic algorithm based on compound mutation (denoted by BCM-GA, for short). Further, we discuss the global convergence of BCM-GA by using the Markov chain theory, and analyze the performance of BCM-GA through an example. All the results indicate that, BCM-GA is obviously higher than real number code genetic algorithm (denoted by B(10)GA, for short) in the convergence time and convergence precision.

关键词： Genetic algorithm real coding mutation Mutation criteria function compound mutation strategy Markov chain

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Genetic algorithm-based RBF neural network load forecasting model

Genetic algorithm-based RBF neural network load forecasting ...

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IEEE-Power-Engineering-Society General Meeting

作者： Yang, Zhangang Che, Yanbo Cheng, K. W. Eric Tianjin Univ Sch Elect Engn & Automat Tianjin 300072 Peoples R China Hong Kong Polytech Univ Dept Elect Engn Hong Kong Peoples R China

ISBN: (纸本)9781424412969

To overcome the limitation of the traditional load forecasting method, a new load forecasting system basing on radial basis Gaussian kernel function (RBF) neural network is proposed in this paper. Genetic algorithm adopting the real coding, crossover probability and mutation probability was applied to optimize the parameters of the neural network, and a faster convergence rate was reached. Theoretical analysis and simulations prove that this load forecasting model is more practical and has more precision than the traditional one.

关键词： load forecasting RBF neural network real coding genetic algorithm convergence rate

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A real-coded genetic algorithm for two-mode KL-means partitioning with application to homogeneity blockmodeling

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SOCIAL NETWORKS 2015年 41卷 26-35页

作者： Brusco, Michael Doreian, Patrick Florida State Univ Tallahassee FL 32306 USA Univ Ljubljana Ljubljana 61000 Slovenia Univ Pittsburgh Pittsburgh PA 15260 USA

The two-mode KL-means partitioning (TMKLMP) problem has a number of important applications in the social and physical sciences. For example, the intra-block variability measure associated with TMKLMP underscores its direct relevance to two-mode homogeneity blockmodeling of binary and real-valued social networks. We present a real-coded genetic algorithm for obtaining TMKLMP solutions. A simulation study showed that the new algorithm compares favorably to a multistart implementation of a two-mode KL-means heuristic, which is recognized as a top-performing method for TMKLMP. The merit of the proposed method is demonstrated via an application to the blockmodeling of social network data associated with signing of environmental advertisements in the New York Times as a part of the Turning Point Project. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Blockmodeling Clustering Genetic algorithm Homogeneity blockmodeling real coding Two-mode KL-means partitioning

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