Simulating accepting networks of evolutionary processors with filtered connections by accepting evolutionary P systems

In this work, we propose a variant of P system based on the rewriting of string-objects by means of evolutionary rules. The membrane structure of such a P system seems to be a very natural tool for simulating the filters in accepting networks of evolutionary processors with filtered connections. We discuss an informal construction supporting this simulation. A detailed proof is to be considered in an extended version of this work

Two-Step Incremental Evolution of a Prosthetic Hand Controller Based on Digital Logic Gates

Evolvable Hardware (EHW) has been proposed as a new method for designing systems for real-world applications. In this paper it is applied for evolving a prosthetic hand controller. It is shown that better generalization performance than neural networks can be obtained. The proposed architecture is based on digital logic gates and its configuration is determined by two separate steps of evolution.

Artificial Life, Death and Epidemics in Evolutionary, Generative Electronic Art

Abstract. This paper explores strategies for slowing the onset of convergence in an evolving population of agents. The strategies include the emergent maintenance of separate agent sub-populations and migration between them, and the introduction of virtual diseases that co-evolve parasitically within their hosts. The method looks to Artificial Life and epidemiology for its inspiration but its ultimate concerns are in studying epidemics as a process suitable for application to generative electronic art. The simulation is used to construct a prototype artwork for a fully interactive stereoscopic virtual-reality environment to be exhibited in a science museum. 1 Motivation and Past Work Evolutionary, ecological simulations often converge with the population predominantly of similar genetic composition. For generative electronic art this may have undesirable aesthetic consequences, especially if the desired result is an exploration of diverse audio or visual solutions within the constraints of the work. For instance, it may be desirable that diverse visual and sonic forms be present in a simulated environmen

A Robust Neural Network Based Object Recognition System and Its SIMD Implementation

Recognition of objects is a particularly demanding problem, if one considers that each image must be interpreted in milliseconds (usually 30 or 40 frames/second). The problem becomes more difficult if the objects are distorted and/or partially occluded. In this case a sequence of local features are to be extracted, combined in a global shape description and classified as belonging to pre-defined sets of known shapes (reference shapes). In this paper we propose a massively parallel object recognition system, which makes use of the multi polygonal approximation scheme for the extraction of rotation and translation invariant shape features, in connection with artificial neural networks for the parallel classification of the extracted features. The system has been successfully applied for recognizing aircraft shapes in different sizes, orientations, with the addition of noise distortion and occlusion. Timings on the Connection Machine 200 are also reporte