Analysis of Cellular Automata with Mixed Rules and its Synthesis

This paper studies the synthesis and the stability of the cellular automata with mixed rules(MCA). Although the dynamics of MCA are determined by rule tables, the number of rule tables is enormous and brute-force search is almost impossible. We propose three synthesis methods based on the genetic algorithm (GA) which is related to the stability.First, we analyze the stability of the teacher signal which is a periodic control signal of AC/DC converter. The fitness of GA is the local stability of the teacher signal.Second, there are several isolated spatiotemporal patterns without transient phenomena by using the elementary cellular automata (ECA). We analyze the stability of those patterns by using MCA and GA. The fitness of GA is the global stability of an isolated pattern.Finally, there are only isolated spatiotemporal patterns without transient phenomena by using ECA. We synthesize those patterns and analyze the stability. The fitness of GA is the number of times to enlarge transient states for steady states.修士(工学)法政大学 (Hosei University

A Family of Controllable Cellular Automata for Pseudorandom Number Generation

In this paper, we present a family of novel Pseudorandom Number Generators (PRNGs) based on Controllable Cellular Automata (CCA) ─ CCA0, CCA1, CCA2 (NCA), CCA3 (BCA), CCA4 (asymmetric NCA), CCA5, CCA6 and CCA7 PRNGs. The ENT and DIEHARD test suites are used to evaluate the randomness of these CCA PRNGs. The results show that their randomness is better than that of conventional CA and PCA PRNGs while they do not lose the structure simplicity of 1-d CA. Moreover, their randomness can be comparable to that of 2-d CA PRNGs. Furthermore, we integrate six different types of CCA PRNGs to form CCA PRNG groups to see if the randomness quality of such groups could exceed that of any individual CCA PRNG. Genetic Algorithm (GA) is used to evolve the configuration of the CCA PRNG groups. Randomness test results on the evolved CCA PRNG groups show that the randomness of the evolved groups is further improved compared with any individual CCA PRNG

Heredity, Complexity, and Surprise: Embedded Self-Replication and Evolution in CA

Abstract. This paper reviews the history of embedded, evolvable selfreplicating structures implemented as cellular automata systems. We relate recent advances in this field to the concept of the evolutionary growth of complexity, a term introduced by McMullin to describe the central idea contained in von Neumann's self-reproducing automata theory. We show that conditions for such growth are in principle satisfied by universal constructors, yet that in practice much simpler replicators may satisfy scaled-down -yet equally relevant -versions thereof. Examples of such evolvable self-replicators are described and discussed, and future challenges identified

Development of a Large-Scale Integrated Neurocognitive Architecture - Part 2: Design and Architecture

In Part 1 of this report, we outlined a framework for creating an intelligent agent based upon modeling the large-scale functionality of the human brain. Building on those results, we begin Part 2 by specifying the behavioral requirements of a large-scale neurocognitive architecture. The core of our long-term approach remains focused on creating a network of neuromorphic regions that provide the mechanisms needed to meet these requirements. However, for the short term of the next few years, it is likely that optimal results will be obtained by using a hybrid design that also includes symbolic methods from AI/cognitive science and control processes from the field of artificial life. We accordingly propose a three-tiered architecture that integrates these different methods, and describe an ongoing computational study of a prototype 'mini-Roboscout' based on this architecture. We also examine the implications of some non-standard computational methods for developing a neurocognitive agent. This examination included computational experiments assessing the effectiveness of genetic programming as a design tool for recurrent neural networks for sequence processing, and experiments measuring the speed-up obtained for adaptive neural networks when they are executed on a graphical processing unit (GPU) rather than a conventional CPU. We conclude that the implementation of a large-scale neurocognitive architecture is feasible, and outline a roadmap for achieving this goal

Analysis, design and implementation of front-end reconfigurable antenna systems (FERAS)

The increase in demand on reconfigurable systems and especially for wireless communications applications has stressed the need for smart and agile RF devices that sense and respond to the RF changes in the environment. Many different applications require frequency agility with software control ability such as in a cognitive radio environment where antenna systems have to be designed to fulfill the extendable and reconfigurable multi-service and multi-band requirements. Such applications increase spectrum efficiency as well as the power utilization in modern wireless systems. The emphasis of this dissertation revolves around the following question: Is it possible to come up with new techniques to achieve reconfigurable antenna systems with better performance?\u27 Two main branches constitute the outline of this work. The first one is based on the design of reconfigurable antennas by incorporating photoconductive switching elements in order to change the antenna electrical properties. The second branch relies on the change in the physical structure of the antenna via a rotational motion. In this work a new photoconductive switch is designed with a new light delivery technique. This switch is incorporated into new optically pumped reconfigurable antenna systems (OPRAS). The implementation of these antenna systems in applications such as cognitive radio is demonstrated and discussed. A new radio frequency (RF) technique for measuring the semiconductor carrier lifetime using optically reconfigurable transmission lines is proposed. A switching time investigation for the OPRAS is also accomplished to better cater for the cognitive radio requirements. Moreover, different reconfiguration mechanisms are addressed such as physical alteration of antenna parts via a rotational motion. This technique is supported by software to achieve a complete controlled rotatable reconfigurable cognitive radio antenna system. The inter-correlation between neural networks and cellular automata is also addressed for the design of reconfigurable and multi-band antenna systems for various applications.\u2

Automatic Discovery of Self-Replicating Structures in Cellular Automata

Previous computational models of self-replication using cellular automata have been manually designed, a difficult and time-consuming process. We show here how genetic algorithms can be applied to automatically discover rules governing self-replicating structures. The main difficulty in this problem lies in the choice of the fitness evaluation technique. The solution we present is based on a multiobjective fitness function consisting of three independent measures: growth in number of components, relative positioning of components, and the multiplicity of replicants. We introduce a new paradigm for cellular automata models with weak rotational symmetry, called orientation insensitive input, and hypothesize that it facilitates discovery of self-replicating structures by reducing search-space sizes. Experimental yields of self-replicating structures discovered using our technique are shown to be statistically significant. The discovered self-replicating structures compare favorably in terms of simplicity with those generated manually in the past, but differ in unexpected ways. These results suggest that further exploration in the space of possible self-replicating structures will yield additional new structures. Furthermore, this research sheds light on the process of creating self-replicating structures, opening the door to future studies on the discovery of novel self-replicating molecules and self-replicating assemblers in nanotechnology

Automatic Discovery of Self-Replicating Structures in Cellular Automata

Automatic Discovery of Self-Replicating Structures in Cellular Automat