Spatially Structured Evolutionary Algorithms
Artificial Evolution in Space and Time (Natural Computing Series)
By Marco Tomassini
"Spatially Structured Evolutionary Algorithms" is published by Springer in October 25, 2005, it has 193 pages and the language of the book is English.
“Spatially Structured Evolutionary Algorithms” Metadata:
- Title: ➤ Spatially Structured Evolutionary Algorithms
- Author: Marco Tomassini
- Language: English
- Number of Pages: 193
- Publisher: Springer
- Publish Date: October 25, 2005
“Spatially Structured Evolutionary Algorithms” Subjects and Themes:
- Subjects: ➤ Evolutionary programming (Computer science) - Evolutionary computation - Genetic algorithms - Computer science - Computer software - Electronic data processing - Computational complexity - Artificial intelligence - Computation by Abstract Devices - Algorithm Analysis and Problem Complexity - Numeric Computing - Discrete Mathematics in Computer Science - Artificial Intelligence (incl. Robotics)
Edition Specifications:
- Format: Hardcover
- Weight: 14.1 ounces
- Dimensions: 9.4 x 6.3 x 0.8 inches
Edition Identifiers:
- The Open Library ID: OL9055232M - OL9075035W
- Online Computer Library Center (OCLC) ID: 62902448
- Library of Congress Control Number (LCCN): 2005929365
- ISBN-13: 9783540241935
- ISBN-10: 3540241930
- All ISBNs: 3540241930 - 9783540241935
AI-generated Review of “Spatially Structured Evolutionary Algorithms”:
"Spatially Structured Evolutionary Algorithms" Description:
The Open Library:
Evolutionary algorithms (EAs) is now a mature problem-solving family of heuristics that has found its way into many important real-life problems and into leading-edge scientific research. Spatially structured EAs have different properties than standard, mixing EAs. By virtue of the structured disposition of the population members they bring about new dynamical features that can be harnessed to solve difficult problems faster and more efficiently. This book describes the state of the art in spatially structured EAs by using graph concepts as a unifying theme. The models, their analysis, and their empirical behavior are presented in detail. Moreover, there is new material on non-standard networked population structures such as small-world networks. The book should be of interest to advanced undergraduate and graduate students working in evolutionary computation, machine learning, and optimization. It should also be useful to researchers and professionals working in fields where the topological structures of populations and their evolution plays a role.
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