A Learning Classifier Systems Approach to Clustering Learning Classifier Systems

Abstract- This paper presents a novel approach to clustering using a simple accuracy-based Learning Classifier System. Our approach achieves this by exploiting the evolutionary computing and reinforcement learning techniques inherent to such systems. The purpose of the work is to develop an approach to learning rules which accurately describe clusters without prior assumptions as to their number within a given dataset. Favourable comparisons to the commonly used k-means algorithm are demonstrated on a number of datasets.

Genetic Algorithms for Generating Minimum Path Configurations

this paper we will restrict attention to systems that support a maximum of four links per processor, but the principles described are equally applicable to any number of links per processor. The distance between any two processors may be measured as the number of links a message has to traverse between its source processor and the destination processor. This increase in maximum distance between any two processors is a function of the underlying interconnection strategy. The philosophy underlying the construction of a minimum path (AMP) configuration is to minimize the diameter, d max , of the interconnection network; that is, to minimize the maximum number of links a message has to travel between any source processor and any other destination processor within the configuration. This principle is maintained even at the expense of the loss of regularity in a system. Due to their irregular nature, the problem of finding optimum AMP configurations is a large one and is a formidable task even for traditional heuristic depth-first search strategies [4], prompting Steve Gregory's quote given above. For example, from the initial spanning tree of a 32 processor AMP (see section 2.1), there are 66 links that need to be connected givin

Towards Machine Learning Control of Chemical Computers

The behaviour of pulses of Belousov-Zhabotinski (BZ) reaction-diffusion waves can be controlled automatically through machine learning. By extension, a form of chemical network computing, i.e., a massively parallel non-linear computer, can be realised by such an approach. In this initial study a light-sensitive sub-excitable BZ reaction in which a checkerboard image comprising of varying light intensity cells is projected onto the surface of a thin silica gel impregnated with tris(bipyridyl) ruthenium (II) catalyst and indicator is used to make the network. As a catalyst BZ solution is swept past the gel, pulses of wave fragments are injected into the checkerboard grid resulting in rich spatio-temporal behaviour. This behaviour is shown experimentally to be repeatable under the same light projections. A machine learning approach, a learning classifier system, is then shown able to direct the fragments to an arbitrary position through dynamic control of the light intensity within each cell in both simulated and real chemical systems. 2