We present results from experiments in which a genetic algorithm (GA) was used to evolve cellular automata (CAs) to perform a particular computational task—one-dimensional density classification. We look in detail at the evolutionary mechanisms producing the GA’s behavior on this task and the impediments faced by the GA. In particular, we identify four “epochs of innovation ” in which new CA strategies for solving the problem are discovered by the GA, describe how these strategies are implemented in CA rule tables, and identify the GA mechanisms underlying their discovery. The epochs are characterized by a breaking of the task’s symmetries on the part of the GA. The symmetry breaking results in a short-term fitness gain but ultimately prevents the discovery of the most highly fit strategies. We discuss the extent to which symmetry breaking and other impediments are general phenomena in any GA search. 1.

We present results from an experiment similar to one performed by Packard [24], in which a genetic algorithm is used to evolve cellular automata (CA) to perform a particular computational task. Packard examined the frequency of evolved CA rules as a function of Langton’s λ parameter [17], and interpreted the results of his experiment as giving evidence for the following two hypotheses: (1) CA rules able to perform complex computations are most likely to be found near “critical ” λ values, which have been claimed to correlate with a phase transition between ordered and chaotic behavioral regimes for CA; (2) When CA rules are evolved to perform a complex computation, evolution will tend to select rules with λ values close to the critical values. Our experiment produced very different results, and we suggest that the interpretation of the original results is not correct. We also review and discuss issues related to λ, dynamical-behavior classes, and computation in CA. The main constructive results of our study are identifying the emergence and competition of computational strategies and analyzing the central role of symmetries in an evolutionary system. In particular, we demonstrate how symmetry breaking can impede the evolution toward higher computational capability.

The long term goal is to create a ‘paintable computer ’ — an instance of several thousand copies of a single integrated circuit (IC), each the size of a large sand kernel, uniformly distributed in a semi-viscous medium and applied to a surface like paint. Each IC contains an embedded micro, memory and a wireless transceiver in a 4 mm 2 package, is internally clocked, and communicates locally. While the hardware presents its own challenges, the deeper problems lie in the programming model. In this research, we develop a candidate programming model and qualify its performance over a set of representative applications. Work begins with a hardware reference model for the individual computing particles. A first cut programming model is proposed and initial applications are developed. Results from the applications are fed back to drive an iterative refinement of the programming model, followed by a succeeding rounds of application development. Preliminary thesis statement: “A programming model employing a selforganizing ecology of mobile code fragments supports a variety of useful applications on a paintable computer

Abstract. How does evolution produce sophisticated emergent computation in systems composed of simple components limited to local interactions? To model such a process, we used a genetic algorithm (GA) to evolve cellular automata to perform a computational task requiring globally-coordinated information processing. On most runs a class of relatively unsophisticated strategies was evolved, but on a subset of runs a number of quite sophisticated strategies was discovered. We analyze the emergent logic underlying these strategies in terms of information processing performed by “particles ” in space-time, and we describe in detail the generational progression of the GA evolution of these strategies. Our analysis is a preliminary step in understanding the general mechanisms by which sophisticated emergent computational capabilities can be automatically produced in decentralized multiprocessor systems. 1.

A computer is a physical system which has a very general ability to simulate other physical systems (and in particular, other computers). In this paper we investigate the question of whether microscopic quantum systems can be computers. Using a reversible cellular automaton model of computation we illustrate several approaches to this question. We then attempt to extend Feynman’s construction of a quantum computer in order to arrive at a quantum model of parallel processing. 1

to be published in the special issue of Computer & Chemistry

Complex models may have model components distributed over a network and generally require significant execution times. The field of parallel and distributed simulation has grown over the past fifteen years to accommodate the need of simulating the complex models using a distributed versus sequential method. In particular, asynchronous parallel discrete event simulation (PDES) has been widely studied, and yet we envision greater acceptance of this methodology as more readers are exposed to PDES introductions that carefully integrate real-world applications. With this in mind, we present two key methodologies (con- servative and optimistic) which have been adopted as solutions to PDES systems. We discuss PDES terminology and methodology under the umbrella of the personal communications services application.

Abstract. The use of object-orientation for both spatial data and spatial process models facilitates their integration, which can allow exploration and explanation of spatial-temporal phenomena. In order to better understand how tight coupling might proceed and to evaluate the possible functional and efficiency gains from such a tight coupling, we identify four key relationships affecting how geographic data (fields and objects) and agent-based process models can interact: identity, causal, temporal and topological. We discuss approaches to implementing tight integration, focusing on a middleware approach that links existing GIS and ABM development platforms, and illustrate the need and approaches with example agent-based models. Key words: Object-orientation, Agent-based models, Spatial-temporal modeling, Topology

Cellular automata (CAs) are decentralized spatially extended systems consisting of large numbers of simple identical components with local connectivity. Such systems have the potential to perform complex computations with a high degree of efficiency and robustness, as well as to model the behavior of complex systems in nature. For these reasons CAs and related architectures have

In this paper we review previous work and present new work concerning the relationship between dynamical systems theory and computation. In particular, we review work by Langton [21] and Packard [29] on the relationship between dynamical behavior and computational capability in cellular automata (CAs). We present results from an experiment similar to the one described by Packard [29], which was cited as evidence for the hypothesis that rules capable of performing complex computations are most likely to be found at a phase transition between ordered and chaotic behavioral regimes for CAs (the “edge of chaos”). Our experiment produced very different results from the original experiment, and we suggest that the interpretation of the original results is not correct. We conclude by discussing general issues related to dynamics, computation, and the “edge of chaos ” in cellular automata. 1