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- Peter A. N. Bosman, Dirk Thierens
- IEEE Trans. Evolutionary Computation
- 2003

Over the last decade, a variety of evolutionary algorithms (EAs) have been proposed for solving multi–objective optimization problems. Especially more recent multi–objective evolutionary algorithms (MOEAs) have been shown to be efficient and superior to earlier approaches. In the development of new MOEAs, the strive is to obtain increasingly better… (More)

- Dirk Thierens
- GECCO
- 2005

Learning the optimal probabilities of applying an exploration operator from a set of alternatives can be done by self-adaptation or by adaptive allocation rules. In this paper we consider the latter option. The allocation strategies discussed in the literature basically belong to the class of probability matching algorithms. These strategies adapt the… (More)

- Dirk Thierens
- Evolutionary Computation
- 1999

Scalable evolutionary computation has become an intensively studied research topic in recent years. The issue of scalability is predominant in any field of algorithmic design, but it became particularly relevant for the design of competent genetic algorithms once the scalability problems of simple genetic algorithms were understood. Here we present some of… (More)

- Dirk Thierens
- PPSN
- 2010

- Dirk Thierens, Peter A. N. Bosman
- GECCO
- 2011

A key search mechanism in Evolutionary Algorithms is the mixing or juxtaposing of partial solutions present in the parent solutions. In this paper we look at the efficiency of mixing in genetic algorithms (GAs) and estimation-of-distribution algorithms (EDAs). We compute the mixing probabilities of two partial solutions and discuss the effect of the… (More)

- Dirk Thierens, David E. Goldberg
- ICGA
- 1993

- Peter A. N. Bosman, Dirk Thierens
- GECCO
- 1999

The last few years there has been an increasing amount of interest in the field of distribution estimation optimization algorithms. As more techniques are introduced, the variety in tested distribution structures increases. In this paper we analyze the implications of the form of such a structure. We show that learning the linkage relations alone and using… (More)

- Dirk Thierens
- 2002

Abstract The adaptation of mutation rate parameter values is important to allow the search process to optimize its performance during run time. In addition it frees the user of the need to make non-trivial decisions beforehand. Contrary to real vector coded genotypes, for discrete genotypes most users still prefer to use a fixed mutation rate. Here we… (More)

- Dirk Thierens
- Parameter Setting in Evolutionary Algorithms
- 2007

- Peter A. N. Bosman, Dirk Thierens
- PPSN
- 2000

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