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| − | |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 | + | |Abstract= Most research in evolutionary computation focuses on optimization of |
| − | that are capable of continuously adapting the solution to a changing | + | static, non-changing problems. Many real-world optimization |
| − | environment. If the optimization problem is dynamic, the goal is no | + | problems, however, are dynamic, and optimization methods are needed |
| − | longer to find the extrema, but to track their progression through | + | that are capable of continuously adapting the solution to a changing |
| − | the space as closely as possible. In this chapter, we suggest a | + | environment. If the optimization problem is dynamic, the goal is no |
| − | classification of dynamic optimization problems, and survey and | + | longer to find the extrema, but to track their progression through |
| − | classify a number of the most widespread techniques that have been | + | the space as closely as possible. In this chapter, we suggest a |
| − | published in the literature so far to make evolutionary algorithms | + | classification of dynamic optimization problems, and survey and |
| − | suitable for changing optimization problems. After this | + | classify a number of the most widespread techniques that have been |
| − | introduction to the basics, we will discuss in more detail two | + | published in the literature so far to make evolutionary algorithms |
| − | specific approaches, pointing out their deficiencies and potential. | + | suitable for changing optimization problems. After this |
| − | The first approach is based on memorization, the other one is uses | + | introduction to the basics, we will discuss in more detail two |
| − | a novel multi-population structure. | + | 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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Aktuelle Version vom 8. September 2009, 11:58 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
Forschungsgebiet
Evolutionäre Optimierung veränderlicher Probleme