#### Filter Results:

- Full text PDF available (18)

#### Publication Year

1999

2014

- This year (0)
- Last 5 years (3)
- Last 10 years (6)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

This is a progress report describing my research during the last one and a half year, performed during part A of my Ph.D. study. The research field is multi-objective optimization using evolutionary algorithms, and the reseach has taken place in a collaboration with Aarhus Univerity, Grundfos and the Alexandra Institute. My research so far has been focused… (More)

- Rasmus K. Ursem
- PPSN
- 2002

Population diversity is undoubtably a key issue in the performance of evolutionary algorithms. A common hypothesis is that high diversity is important to avoid premature convergence and to escape local optima. Various diversity measures have been used to analyze algorithms, but so far few algorithms have used a measure to guide the search. The… (More)

- Rasmus K. Ursem
- 1999

Since practical problems often are very complex with a large number of objectives it can be diicult or impossible to create an objective function expressing all the criterias of good solutions. Sometimes a simpler function can be used where local optimas could be both valid and interesting. Because evolutionary algorithms are population based they have the… (More)

- Rasmus K. Ursem
- GECCO
- 2000

In recent years, optimization algorithms have received increasing attention by the research community as well as the industry. In the area of evolutionary computation (EC), inspiration for optimization algorithms originates in Darwin’s ideas of evolution and survival of the fittest. Such algorithms simulate an evolutionary process where the goal is to… (More)

- Rasmus K. Ursem
- 2003

Parameter identification of system models is a fundamental step in the process of designing a controller for a system. In control engineering, a wide selection of analytic identification techniques exists for linear systems, but not for non-linear systems. Instead, the model parameters may be determined by an optimization algorithm by minimizing the error… (More)

- Rasmus K. Ursem, Thiemo Krink, Mikkel T. Jensen, Zbigniew Michalewicz
- IEEE Trans. Evolutionary Computation
- 2002

Most applications of evolutionary algorithms (EAs) deal with static optimization problems. However, in recent years, there has been a growing interest in timevarying (dynamic) problems, which are typically found in real-world scenarios. One major challenge in this field is the design of realistic test-case generators, which requires a systematic analysis of… (More)

- Rasmus K. Ursem, Pierré Vadstrup
- Appl. Soft Comput.
- 2004

In the area of optimization, applied research typically focuses on finding the best possible solution to the practical problem at hand. In contrast, a large part of basic research aims at developing novel algorithms with improved performance. In practical application, most studies employ rather simple algorithms. On the other hand, most novel algorithms are… (More)

The setting of parameters in Evolutionary Algorithms (EA) has crucial influence on their performance. Typically, the best choice depends on the optimization task. Some parameters yield better results when they are varied during the run. Recently, the so-called TerrainBased Genetic Algorithm (TBGA) was introduced, which is a self-tuning version of the… (More)

- Rasmus K. Ursem
- 2001