#### Filter Results:

#### Publication Year

2002

2010

#### Co-author

#### Key Phrase

#### Publication Venue

Learn More

Estimation of Distribution Algorithms (EDA) have been proposed as an extension of genetic algorithms. In this paper we explain the relationship of EDA to algorithms developed in statistics, artificial intelligence, and statistical physics. The major design issues are discussed within a general interdisciplinary framework. It is shown that maximum entropy… (More)

- Robin Höns
- 2006

In the field of optimization using probabilistic models of the search space, this thesis identifies and elaborates several advancements in which the principles of maximum en-tropy and minimum relative entropy from information theory are used to estimate a probability distribution. The probability distribution within the search space is represented by a… (More)

Estimation of Distribution Algorithms (EDA) have been proposed as an extension of genetic algorithms. In this paper the major design issues of EDA's are discussed using an interdisciplinary framework, the minimum relative entropy (M inRel) approximation. We assume that the function to be optimized is additively decomposed (ADF). The interaction graph GADF… (More)

Estimation of Distribution Algorithms (EDA) have been proposed as an extension of genetic algorithms. In this paper the major design issues of EDA's are discussed within a general interdisciplinary framework, the <i>maximum entropy</i> approximation. Our EDA algorithm <i>FDA</i> assumes that the function to be optimized is additively decomposed (ADF). The… (More)

We investigate theoretically a voter model-or how opinion changes within a community, subject to neighborhood interaction. Under Markov chain modeling, the stationary probability distribution for certain cases is determined in simple analytical form, then its entropy is analyzed.

- ‹
- 1
- ›