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Swarm Intelligence For Multi Objective Problems In Data Mining (Studies In Computational Intelligence)

Swarm Intelligence For Multi Objective Problems In Data Mining (Studies In Computational Intelligence)

Carlos A. Coello
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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 interacting locallywitheachotherandwiththeirenvironment.Suchinteractionsproducean emergentbehaviorthatismodelledinacomputerinordertosolveproblems.The two most popular metaheuristics within swarm intelligence are particle swarm optimization and ant colony optimization . 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.
Language
English
Pages
287
Format
Hardcover
Publisher
Springer
Release
September 28, 2009
ISBN
3642036244
ISBN 13
9783642036248

Swarm Intelligence For Multi Objective Problems In Data Mining (Studies In Computational Intelligence)

Carlos A. Coello
0/5 ( ratings)
Read Download
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 interacting locallywitheachotherandwiththeirenvironment.Suchinteractionsproducean emergentbehaviorthatismodelledinacomputerinordertosolveproblems.The two most popular metaheuristics within swarm intelligence are particle swarm optimization and ant colony optimization . 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.
Language
English
Pages
287
Format
Hardcover
Publisher
Springer
Release
September 28, 2009
ISBN
3642036244
ISBN 13
9783642036248

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