Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition (Adaptation, Learning, and Optimization)

Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition (Adaptation, Learning, and Optimization)

Language: English

Pages: 321

ISBN: 3642378455

Format: PDF / Kindle (mobi) / ePub

For many engineering problems we require optimization processes with dynamic adaptation as we aim to establish the dimension of the search space where the optimum solution resides and develop robust techniques to avoid the local optima usually associated with multimodal problems. This book explores multidimensional particle swarm optimization, a technique developed by the authors that addresses these requirements in a well-defined algorithmic approach.


After an introduction to the key optimization techniques, the authors introduce their unified framework and demonstrate its advantages in challenging application domains, focusing on the state of the art of multidimensional extensions such as global convergence in particle swarm optimization, dynamic data clustering, evolutionary neural networks, biomedical applications and personalized ECG classification, content-based image classification and retrieval, and evolutionary feature synthesis. The content is characterized by strong practical considerations, and the book is supported with fully documented source code for all applications presented, as well as many sample datasets.


The book will be of benefit to researchers and practitioners working in the areas of machine intelligence, signal processing, pattern recognition, and data mining, or using principles from these areas in their application domains. It may also be used as a reference text for graduate courses on swarm optimization, data clustering and classification, content-based multimedia search, and biomedical signal processing applications.


















thus engineers are people who work with engines. In fact, the word ‘‘engineer’’ comes from the French word ‘‘ingénieur’’ which derives from the same Latin roots as the words ‘‘ingenuity’’ and ‘‘genius’’. Therefore, ‘‘Optimization’’ is the very essence of engineering as engineers (at least the good ones) are not interested with any solution of a given problem, but the best possible or as fully perfect, functional, or effective as possible one. In short, engineering is the art of creating optimum

shall show that MD PSO evolves to optimum or near-optimum networks in general and has a superior generalization capability. Furthermore, MD PSO naturally favors a low-dimension solution when it exhibits a competitive performance with a high dimension counterpart and such a native tendency eventually leads the evolution process to favor compact network configurations in the architecture space rather than the complex ones, as long as optimality prevails. The main application area of this elegant

calculate the area under any function. Therefore, this work contains the first clear statement of the Fundamental Theorem of Calculus. As Newton did not publish his findings until 1687, unaware that he had discovered similar methods, Leibniz developed his calculus in Paris between 1673 and 1676. InR November 1675, he wrote a manuscript using the common integral notation, f ð xÞ dx; for the first time. The following year, he discovered the power law of differentiation, dðxn Þ ¼ nxnÀ1 dx for both

shall clarify the use of the member variable m_bScore in Chap. 5. All nonlinear functions are implemented within MYFUN.cpp source file. To perform a PSO operation for nonlinear function minimization, a CPSO_MD object should be created with proper template classes: , and initialized with: (1) the default PSO parameters stored in PSOparam _psoDef, and (2) the fitness function ? any nonlinear function within MYFUN.cpp. In short, the entire MD PSO initialization can be summarized

Fig. 7.1 Fig. 7.2 Fig. 7.3 Fig. 7.4 Fig. 7.5 Figures Typical clustering results via MD PSO with FGBF. Over-clustered samples are indicated with *. . . . . . . . . . . . . . Fitness score (top) and dimension (bottom) plots vs. iteration number for a MD PSO with FGBF clustering operation over C4. 3 clustering snapshots at iterations 105, 1,050 and 1,850, are presented below. . . . . . . . . . . . . . . . . . . . . . . Fitness score (top) and dimension (bottom) plots vs. iteration number for a MD

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