CA-NEAT: Evolved Compositional Pattern Producing Networks for Cellular Automata Morphogenesis and Replication

Cellular Automata (CA) are a remarkable example of morphogenetic system, where cells grow and self-organise through local interactions. CA have been used as abstractions of biological development and artificial life. Such systems have been able to show properties that are often desirable but difficult to achieve in engineered systems, e.g. morphogenesis and replication of regular patterns without any form of centralized coordination. However, cellular systems are hard to program (i.e. evolve) and control, especially when the number of cell states and neighbourhood increase. In this paper, we propose a new principle of morphogenesis based on Compositional Pattern Producing Networks (CPPNs), an abstraction of development that has been able to produce complex structural motifs without local interactions. CPPNs are used as Cellular Automata genotypes and evolved with a NeuroEvolution of Augmenting Topologies (NEAT) algorithm. This allows complexification of genomes throughout evolution with phenotypes emerging from self-organisation through development based on local interactions. In this paper, the problems of 2D pattern morphogenesis and replication are investigated. Results show that CA-NEAT is an appropriate means of approaching cellular systems engineering, especially for future applications where natural levels of complexity are targeted. We argue that CA-NEAT could provide a valuable mapping for morphogenetic systems, beyond cellular automata systems, where development through local interactions is desired

Modified Genetic Algorithms for Cellular Automata Design

Tato práce se zabývá evolučním návrhem přechodové funkce celulárního automatu řešícího zvolenou úlohu. Jsou v ní popsány celulární automaty, evoluční algoritmy a alternativní forma zápisu pravidel přechodové funkce vhodná pro evoluční návrh - podmínková pravidla. Dále je zvolen problém řešený celulárním automatem a prezentovány pokusy takový automat navrhnout genetickým algoritmem. Pokračuje se optimalizací parametrů algoritmu, hledáním jeho možných problémů a navržením modifikací řešících je. Pozitivní vlyv těchto modifikací je následně zhodnocen na několika experimentech.This bachelor’s thesis aims to examine possibilities of designing a transition function enabling a cellular automaton to solve a given problem using a genetic algorithm. It contains an introduction to cellular automata, evolution algorithms and conditionally matching rules, a method of descripting a transition function suitable for evolutionary development. A set of experiments is conducted using the standard version of genetic algorithms to determine its optimal configuration. Additionally, modifications of this standard algorithm are proposed, effect of which on the algorithm’s performance is then evaluated by further experiments.

Bits from Biology for Computational Intelligence

Computational intelligence is broadly defined as biologically-inspired computing. Usually, inspiration is drawn from neural systems. This article shows how to analyze neural systems using information theory to obtain constraints that help identify the algorithms run by such systems and the information they represent. Algorithms and representations identified information-theoretically may then guide the design of biologically inspired computing systems (BICS). The material covered includes the necessary introduction to information theory and the estimation of information theoretic quantities from neural data. We then show how to analyze the information encoded in a system about its environment, and also discuss recent methodological developments on the question of how much information each agent carries about the environment either uniquely, or redundantly or synergistically together with others. Last, we introduce the framework of local information dynamics, where information processing is decomposed into component processes of information storage, transfer, and modification -- locally in space and time. We close by discussing example applications of these measures to neural data and other complex systems

New Evolutionary Algorithms for Designing Cellular Automata

Táto práca sa zaoberá evolučným návrhom prechodových funkcií celulárnych automatov postavených na podmienkových pravidlách. Práca predstavuje nový algoritmus ESP a jeho porovnanie s existujúci evolučnými technikami, konkrétne evolučnou stratégiou a genetic- kým algoritmom. Ako prípadové štúdie riešené v navrhovaných celulárnych automatoch, boli zvolené replikujúce sa štruktúry, pohybujúce sa objekty a vývoj vzorov.This thesis describes an evolutionary design of state-transition functions in cellular auto- mata built on conditionally matching rules. It presents a new algorithm ESP and its com- parison with already existing evolutionary techniques, specifically the evolutionary strategy and genetic algorithm. Chosen Case studies include self-replicating structures, moving ob- jects and development of patterns.

Creating Persian-like music using computational intelligence

Dastgāh are modal systems in traditional Persian music. Each Dastgāh consists of a group of melodies called Gushé, classified in twelve groups about a century ago (Farhat, 1990). Prior to that time, musical pieces were transferred through oral tradition. The traditional music productions revolve around the existing Dastgāh, and Gushe pieces. In this thesis computational intelligence tools are employed in creating novel Dastgāh-like music.There are three types of creativity: combinational, exploratory, and transformational (Boden, 2000). In exploratory creativity, a conceptual space is navigated for discovering new forms. Sometimes the exploration results in transformational creativity. This is due to meaningful alterations happening on one or more of the governing dimensions of an item. In combinational creativity new links are established between items not previously connected. Boden stated that all these types of creativity can be implemented using artificial intelligence.Various tools, and techniques are employed, in the research reported in this thesis, for generating Dastgāh-like music. Evolutionary algorithms are responsible for navigating the space of sequences of musical motives. Aesthetical critics are employed for constraining the search space in exploratory (and hopefully transformational) type of creativity. Boltzmann machine models are applied for assimilating some of the mechanisms involved in combinational creativity. The creative processes involved are guided by aesthetical critics, some of which are derived from a traditional Persian music database.In this project, Cellular Automata (CA) are the main pattern generators employed to produce raw creative materials. Various methodologies are suggested for extracting features from CA progressions and mapping them to musical space, and input to audio synthesizers. The evaluation of the results of this thesis are assisted by publishing surveys which targeted both public and professional audiences. The generated audio samples are evaluated regarding their Dastgāh-likeness, and the level of creativity of the systems involved

Representation Techniques for Evolutionary Design of Cellular Automata

Tato práce je zaměřena na experimentální testování různých reprezentací přechodové funkce celulárního automatu. V práci je prezentována výpočetní platforma celulárního automatu. Celulární automat má mnoho potenciálních využití při simulacích různých přírodních jevů, fyzikálních systémů, atd. Jeho paralelní výpočet založený na lokálních bunečných interakcích je však náročný na programování, proto je návrh programu automatu často přenechán evolučním technikám. Evoluční techniky založené na Darwinově teorii evoluce byly už mnohokrát využity pro nalezení stejně dobrých nebo lepších než lidsky navržených řešení různých problémů. Evoluční techniky ale vyžadují speciální zakódování řešených problémů, a právě z toho důvodu jsou reprezentace přechodové funkce celulárního automatu zkoumány. Zkoumané reprezentace zahrnují klasickou tabulkovou reprezentaci, podmínková pravidla a kartézske genetické programování. Testovacím problémem pro určení efektivity reprezentací je funkce druhé mocniny.The aim of this thesis is to experimentally evaluate the performance of several distinct representations of transition functions for cellular automata. Cellular automata have many potential applications for simulating various phenomena (e.g. natural processes, physical systems, etc.). Parallel computation of cellular automata is based on local cell interactions. Such computation, however, may prove difficult to program the CA, which is the reason for applying evolutionary techniques for the design of cellular automata in many cases. Evolutionary algorithms, based on Darwin's theory of evolution, have been used to find human-competitive solutions to many problems. In order to perform the evolutionary design of cellular automata, special encodings of the candidate solutions are often necessary. For this purpose the performance testing of various representations of the transition functions will be investigated. In particular, table representation, conditionally matching rules, and genetic programming will be treated. The problem of square calculations in cellular automata will be considered as a case study.