Titlebook: Information Processing with Evolutionary Algorithms; From Industrial Appl Xindong Wu,Lakhmi Jain,Paul P. Wang Book 2005 Springer-Verlag Lon

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書目名稱Information Processing with Evolutionary Algorithms影響因子(影響力)




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書目名稱Information Processing with Evolutionary Algorithms被引頻次




書目名稱Information Processing with Evolutionary Algorithms被引頻次學(xué)科排名




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書目名稱Information Processing with Evolutionary Algorithms年度引用學(xué)科排名




書目名稱Information Processing with Evolutionary Algorithms讀者反饋




書目名稱Information Processing with Evolutionary Algorithms讀者反饋學(xué)科排名

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Extending the Boundaries of Design Optimization by Integrating Fast Optimization Techniques with Maa combination of machine-code-based, LGP (for modeling) and ES CDSA (for optimization) together provides the best combination of available tools and algorithms for this task..By conceiving of design optimization projects as integrated modeling and optimization problems from the outset, we anticipate

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An Evolutionary Algorithm Based on Morphological Associative Memories for Endmember Selection in Hycation to the task of endmember determination in hyperspectral images. After shifting the hyperspectral image data to the mean and taking the signs of the resulting hyperspectral patterns, we obtain a binary representation of the image pixels. Morphologically independent binary patterns can be seen

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On a Gradient-based Evolution Strategy for Parametric Illumination Correction,s as a parametric model. The model is a linear combination of Legendre polynomials in the 2D or 3D space. The estimated bias is, therefore, a smooth function characterized by a small set of parameters that define a search space of lower dimension than the images. Our work is an enhancement of the PA

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A New Chromosome Codification for Scheduling Problems,a that makes it clear what we consider the basic building blocks of a chromosome, that is the partial schedules of the set of tasks requiring the same resource; and then to design a scheduling policy that maintains as long as possible these partial schedules. The expected utility of this new codific

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Learning Decision Rules by Means of Hybrid-Encoded Evolutionary Algorithms,mains. The algorithm produces a hierarchical set of rules, that is, the rules are sequentially obtained and must be therefore tried in order until one is found whose conditions are satisfied. In addition, the algorithm tries to obtain more understandable rules by minimizing the number of attributes