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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