The Parameter-less Randomized Gravitational Clustering algorithm with online clusters’ structure characterization

The Parameter - less Geneti Algorithm

The Parameter-Less SOM algorithm

One of the biggest problems facing practical applications of Self-Organising Maps (SOM) is their dependence on the learning rate, the size of the neighbourhood function and the decrease of these parameters as training progresses, all of which have to be selected without firm theoretical guidance. This paper introduces a simple modification to the SOM that completely eliminates the learning rate...

Spherical Randomized Gravitational Clustering

Circular data, i.e., data in the form of 'natural' directions or angles are very common in a number of di erent areas such as biological, meteorological, geological, and political sciences. Clustering circular data is not an easy task due to the circular geometry of the data space. Some clustering approaches, such as the spherical k-means, use the cosine distance instead of the euclidean distan...

A parameter-less genetic algorithm

From the user’s point of view, setting the parameters of a genetic algorithm (GA) is far from a trivial task. Moreover, the user is typically not interested in population sizes, crossover probabilities, selection rates, and other GA technicalities. He is just interested in solving a problem, and what he would really like to do, is to hand-in the problem to a blackbox algorithm, and simply press...

The parameter-less genetic algorithm in practice

The parameter-less genetic algorithm was introduced a couple of years ago as a way to simplify genetic algorithm operation by incorporating knowledge of parameter selection and population sizing theory in the genetic algorithm itself. This paper shows how that technique can be used in practice by applying it to a network expansion problem. The existence of the parameter-less genetic algorithm s...