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Titlebook: Genetic Algorithms; Concepts and Designs K. F. Man,K. S. Tang,S. Kwong Textbook 1999 Springer-Verlag London 1999 Text.algorithms.developmen

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发表于 2025-3-21 18:53:32 | 显示全部楼层 |阅读模式
书目名称Genetic Algorithms
副标题Concepts and Designs
编辑K. F. Man,K. S. Tang,S. Kwong
视频video
概述Gives the reader a complete overview of the latest discussions in the application of genetic algorithms to solve engineering problems.Real-world applications engage the reader with the complexities of
丛书名称Advanced Textbooks in Control and Signal Processing
图书封面Titlebook: Genetic Algorithms; Concepts and Designs K. F. Man,K. S. Tang,S. Kwong Textbook 1999 Springer-Verlag London 1999 Text.algorithms.developmen
描述Genetic Algorithms (GA) as a tool for a search and optimizing methodology has now reached a mature stage. It has found many useful applications in both the scientific and engineering arenas. The main reason for this success is undoubtedly due to the advances that have been made in solid-state microelectronics fabrication that have, in turn, led to the proliferation of widely available, low cost, and speedy computers. The GA works on the Darwinian principle of natural selection for which the noted English philosopher, Herbert Spencer coined the phrase "Survival of the fittest". As a numerical optimizer, the solutions obtained by the GA are not mathematically oriented. Instead, GA possesses an intrinsic flexibility and the freedom to choose desirable optima according to design specifications. Whether the criteria of concern be nonlinear, constrained, discrete, multimodal, or NP hard, the GA is entirely equal to the challenge. In fact, because of the uniqueness of the evolutionary process and the gene structure of a chromosome, the GA processing mechanism can take the form ofparallelism and multiobjective. These provide an extra dimension for solutions where other techniques may have
出版日期Textbook 1999
关键词Text; algorithms; development; fuzzy; hidden markov model; logic; model; multi-objective optimization; optim
版次1
doihttps://doi.org/10.1007/978-1-4471-0577-0
isbn_softcover978-1-85233-072-9
isbn_ebook978-1-4471-0577-0Series ISSN 1439-2232 Series E-ISSN 2510-3814
issn_series 1439-2232
copyrightSpringer-Verlag London 1999
The information of publication is updating

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Intrinsic Characteristics,esults obtained are quite good and are considered to be compatible to those derived from other techniques. However, a simply GA has difficulty in tackling complicated, multi-tasking and conflicting problems, and the speed of computation is generally regarded as slow. To enhance the capability of GA
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Genetic Algorithms in Filtering, the computational power is ensured. In the case of real time processing, this requirement is necessary and obvious. Achieving this goal is by no means easy. This can be very involved in circumstances where the performance criteria are discrete, complex, multiobjective and very often, nonlinear.
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Hierarchical Genetic Algorithms in Computational Intelligence,ligence (CI). This trend of development is understandable since computing power has become so much faster and cheaper nowadays, such that a required solution can be automatically obtained even when this is based upon a computationally intensive scheme.
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Genetic Algorithms in Speech Recognition Systems,ot only enhances the speed of recognition tremendously, but also improves the quality of the overall performance in recognizing the speech utterance. In general, there are two classic approaches for this development, namely, Dynamic Time Warping (DTW) and the Hidden Markov Model (HMM).
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Genetic Algorithms in Communication Systems, These issues involve nonlinear and discrete functions for optimization which may not yield the adequate solutions easily using the classical gradient type of searching tools. The problem can be more severe when multiobjective functions are encountered.
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