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− | |Abstract= Most research in evolutionary computation focuses on optimization of | + | |Abstract= Most research in evolutionary computation focuses on optimization of static, non-changing problems. Many real-world optimization problems, however, are dynamic, and optimization methods are needed |
− | + | that are capable of continuously adapting the solution to a changing | |
− | + | environment. If the optimization problem is dynamic, the goal is no | |
− | + | longer to find the extrema, but to track their progression through | |
− | + | the space as closely as possible. In this chapter, we suggest a | |
− | + | classification of dynamic optimization problems, and survey and | |
− | + | classify a number of the most widespread techniques that have been | |
− | + | published in the literature so far to make evolutionary algorithms | |
− | + | suitable for changing optimization problems. After this | |
− | + | introduction to the basics, we will discuss in more detail two | |
− | + | specific approaches, pointing out their deficiencies and potential. | |
− | + | The first approach is based on memorization, the other one is uses | |
− | + | a novel multi-population structure. | |
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|VG Wort-Seiten= | |VG Wort-Seiten= | ||
|Projekt= | |Projekt= |
Version vom 16. August 2009, 11:09 Uhr
Veröffentlicht: 2002
Herausgeber: Tsutsui, S.; Ghosh, A.
Buchtitel: Theory and Application of Evolutionary Computation: Recent Trends
Seiten: 239-262
Verlag: Springer
BibTeX
Kurzfassung
Most research in evolutionary computation focuses on optimization of static, non-changing problems. Many real-world optimization problems, however, are dynamic, and optimization methods are needed
that are capable of continuously adapting the solution to a changing
environment. If the optimization problem is dynamic, the goal is no
longer to find the extrema, but to track their progression through
the space as closely as possible. In this chapter, we suggest a
classification of dynamic optimization problems, and survey and
classify a number of the most widespread techniques that have been
published in the literature so far to make evolutionary algorithms
suitable for changing optimization problems. After this
introduction to the basics, we will discuss in more detail two
specific approaches, pointing out their deficiencies and potential.
The first approach is based on memorization, the other one is uses
a novel multi-population structure.
Evolutionäre Optimierung veränderlicher Probleme