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{{Publikation Details
 
{{Publikation Details
|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
  static, non-changing problems.  Many real-world optimization
+
that are capable of continuously adapting the solution to a changing
  problems, however, are dynamic, and optimization methods are needed
+
environment.  If the optimization problem is dynamic, the goal is no
  that are capable of continuously adapting the solution to a changing
+
longer to find the extrema, but to track their progression through
  environment.  If the optimization problem is dynamic, the goal is no
+
the space as closely as possible.  In this chapter, we suggest a
  longer to find the extrema, but to track their progression through
+
classification of dynamic optimization problems, and survey and
  the space as closely as possible.  In this chapter, we suggest a
+
classify a number of the most widespread techniques that have been
  classification of dynamic optimization problems, and survey and
+
published in the literature so far to make evolutionary algorithms
  classify a number of the most widespread techniques that have been
+
suitable for changing optimization problems.  After this
  published in the literature so far to make evolutionary algorithms
+
introduction to the basics, we will discuss in more detail two
  suitable for changing optimization problems.  After this
+
specific approaches, pointing out their deficiencies and potential.
  introduction to the basics, we will discuss in more detail two
+
The first approach is based on memorization, the other one is uses
  specific approaches, pointing out their deficiencies and potential.
+
a novel multi-population structure.
  The first approach is based on memorization, the other one is uses
 
  a novel multi-population structure.
 
 
 
 
|VG Wort-Seiten=
 
|VG Wort-Seiten=
 
|Projekt=
 
|Projekt=

Version vom 16. August 2009, 11:09 Uhr


Designing evolutionary algorithms for dynamic optimization problems




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.



Forschungsgruppe

Effiziente Algorithmen


Forschungsgebiet

Evolutionäre Optimierung veränderlicher Probleme