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Books > Computing & IT > Applications of computing > Artificial intelligence
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Fuzzy Evolutionary Computation (Hardcover, 1997 ed.)
Loot Price: R4,744
Discovery Miles 47 440
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Fuzzy Evolutionary Computation (Hardcover, 1997 ed.)
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As of today, Evolutionary Computing and Fuzzy Set Computing are two
mature, wen -developed, and higbly advanced technologies of
information processing. Bach of them has its own clearly defined
research agenda, specific goals to be achieved, and a wen setUed
algorithmic environment. Concisely speaking, Evolutionary Computing
(EC) is aimed at a coherent population -oriented methodology of
structural and parametric optimization of a diversity of systems.
In addition to this broad spectrum of such optimization
applications, this paradigm otTers an important ability to cope
with realistic goals and design objectives reflected in the form of
relevant fitness functions. The GA search (which is often regarded
as a dominant domain among other techniques of EC such as
evolutionary strategies, genetic programming or evolutionary
programming) delivers a great deal of efficiency helping navigate
through large search spaces. The main thrust of fuzzy sets is in
representing and managing nonnumeric (linguistic) information. The
key notion (whose conceptual as weH as algorithmic importance has
started to increase in the recent years) is that of information
granularity. It somewhat concurs with the principle of
incompatibility coined by L. A. Zadeh. Fuzzy sets form a vehic1e
helpful in expressing a granular character of information to be
captured. Once quantified via fuzzy sets or fuzzy relations, the
domain knowledge could be used efficiently very often reducing a
heavy computation burden when analyzing and optimizing complex
systems.
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