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Swarm Intelligence for Multi-objective Problems in Data Mining (Hardcover, 2010 ed.)
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Swarm Intelligence for Multi-objective Problems in Data Mining (Hardcover, 2010 ed.)
Series: Studies in Computational Intelligence, 242
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Multi-objective optimization deals with the simultaneous
optimization of two or more objectives which are normally in
con?ict with each other. Since mul- objective optimization problems
are relatively common in real-world appli- tions, this area has
become a very popular research topic since the 1970s. However, the
use of bio-inspired metaheuristics for solving multi-objective op-
mization problems started in the mid-1980s and became popular until
the mid- 1990s. Nevertheless, the e?ectiveness of multi-objective
evolutionary algorithms has made them very popular in a variety of
domains. Swarm intelligence refers to certain population-based
metaheuristics that are inspired on the behavior of groups of
entities (i.e., living beings) interacting
locallywitheachotherandwiththeirenvironment.Suchinteractionsproducean
emergentbehaviorthatismodelledinacomputerinordertosolveproblems.The
two most popular metaheuristics within swarm intelligence are
particle swarm optimization (which simulates a ?ock of birds
seeking food) and ant colony optimization (which simulates the
behavior of colonies of real ants that leave their nest looking for
food). These two metaheuristics havebecome verypopular
inthelastfewyears,
andhavebeenwidelyusedinavarietyofoptimizationtasks, including some
related to data mining and knowledge discovery in databases.
However, such work has been mainly focused on single-objective
optimization models. The use of multi-objective extensions of swarm
intelligence techniques in data mining has been relatively scarce,
in spite of their great potential, which constituted the main
motivation to produce this book
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