This paper focuses on the phenomena of evolution whose appearance is notable because no explicit mutation, recombination or artificial selection operators are introduced. We call the system self-evolving because every variation is performed by the objects themselves in their machine form. Keywords: artificial chemistry, autocatalytic reaction system, molecular computing, prebiotic evolution, self-organization, self-programming 1

This article reviews the growing body of scientific work in Artificial Chemistry. First, common motivations and fundamental concepts are introduced. Second, current research activities are discussed along three application dimensions: modelling, information processing and optimization. Finally, common phenomena among the different systems are summarized. It is argued here that Artificial Chemistries are "the right stuff" for the study of pre-biotic and bio-chemical evolution, and they provide a productive framework for questions regarding the origin and evolution of organizations in general. Furthermore, Artificial Chemistries have a broad application range to practical problems as shown in this review.

This paper presents a new approach to image feature extraction which utilizes evolutionary autonomous agents. Image features are often mathematically defined in terms of the gray-level intensity at image pixels. The optimality of image feature extraction is to find all the feature pixels from the image. In the proposed approach, the autonomous agents, being distributed computational entities, operate directly in the two-dimensional lattice of a digital image, and exhibit a number of reactive behaviors. In order to effectively locate the feature pixels, individual agents sense the local stimuli from their image environment by means of evaluating the gray-level intensity of locally connected pixels, and accordingly activate their behaviors. The behavioral repository of the agents consists of: (1) featuremarking at local pixels and self-reproduction of offspring agents in the neighboring regions if the local stimuli are found to satisfy feature conditions, (2) diffusion to adjacent image ...

We discuss a system of autocatalytic sequences of binary numbers. Sequences come in two forms, a 1dimensional form (operands) and a 2-dimensional form (operators) that are able to react with each other. The resulting reaction network shows signs of emerging metabolisms. We discuss the general framework and examine specific interactions for a system with strings of length 4 bits. A selfmaintaining network of string types and parasitic interactions are shown to exist. Introduction Published in: Proceedings ARTIFICIAL LIFE IV R. Brooks and P. Maes (Eds.) MIT Press, Cambridge, MA, 1994 pp. 109 --- 118 Sequences of binary numbers are the most primitive form of information storage we know today. They are able to code any kind of man-made information, be it still or moving images, sound waves and other sensory stimulations, be it written language or the rules of mathematics, just to name a few. As the success of vonNeumann computers has shown over the last 50 years, binary sequences are also...

Previous computational models of self-replication in cellular spaces have been manually designed, a very difficult and time-consuming process. This paper introduces the use of genetic algorithms to discover automata rules that govern emergent self-replicating processes. Given dynamically evolving automata, identification of effective fitness functions for self-replicating structures is a difficult task, and we give one solution to this problem. A model consisting of movable automata embedded in a cellular space is introduced and discussed in this context. A genetic algorithm using two fitness criteria was applied to automate rule discovery. After parameter tuning, 6 selfreplicating structures consisting of 2, 3 and 4 automata were discovered over a course of 75 genetic algorithm runs. These results indicate that the fitness functions employed are effective and that genetic algorithms can be used to successfully discover rules for self-replicating structures. 1 Introduction Studying co...

This paper demonstrates a new method of programming artificial chemistries. It uses the emerging capabilities of the system's dynamics for information processing purposes. By evolution of metabolisms that act as control programs for a small robot one achieves the adaptation of the internal metabolic pathways as well as the selection of the most relevant available exteroreceptors. The underlying artificial chemistry evolves efficient information processing pathways with most benefit for the desired task, robot navigation. The results show certain relations to biological systems like motile bacteria.

This thesis demonstrates for the #rst time that it is possible to automatically discover self-replicating structures in cellular space automata models rather than, as has been done in the past, to design them manually. Self-replication is de#ned as the process an entity undergoes in constructing a copy of itself. Von Neumann was the #rst to investigate arti#cial self-replicating structures and did so in the context of cellular automata, a cellular space model consisting of numerous #nite-state machines embedded in a regular tessellation. Interest in arti#cial self-replicating systems has increased in recentyears due to potential applications in molecular-scale manufacturing, programming parallel computing systems, and digital hardware design, and also as part of the #eld of arti#cial life.

cognitive systems, biologicallyinspired computing, artificial life, artificial intelligence, autonomous agents This paper presents a review of the academic literature on biologically-inspired computing approaches to the science and engineering of cognitive systems. This review is intended as a rapid tour through the area (rather than a leisurely wander); and it should be readable in a few hours. The tour is partial in both senses of the word: it is only partially complete, and it is biased (i.e., it is not an

In Chinese document image processing, text and/or graphical block detection serves as an essential step in document layout analysis that in turn permits the effective reasoning about the logical relationships among various text paragraphs and graphical entities for the purpose of document understanding. This paper presents a novel computational paradigm for extracting text/graphic blocks from Chinese document images, which is based on a notion of distributed autonomous agents. The primary features of the agents lie in that they are (1) adaptive to the locality of given images and hence efficient in locating the homogeneous image blocks, (2) reliable in performing image processing as the computation proceeds simultaneously from different image locations, (3) less sensitive to the noise in the given images as the computation disperses gracefully when it is moving away from the homogeneous blocks, and (4) easy to represent in their behaviors and evolvable in their performance. The paper, ...

The evolution of metabolisms that act as control programms for a small robot leads to the selection of most relevant sensory information. The underlying artificial chemistry evolves efficient information processing pathways with most benefit for the desired task, robot navigation. The results show certain relations to biological systems like motile bacteria.