Evolved Art with Transparent, Overlapping, and Geometric Shapes

In this work, an evolutionary art project is presented where images are approximated by transparent, overlapping and geometric shapes of different types, e.g., polygons, circles, lines. Genotypes representing features and order of the geometric shapes are evolved with a fitness function that has the corresponding pixels of an input image as a target goal. A genotype-to-phenotype mapping is therefore applied to render images, as the chosen genetic representation is indirect, i.e., genotypes do not include pixels but a combination of shapes with their properties. Different combinations of shapes, quantity of shapes, mutation types and populations are tested. The goal of the work herein is twofold: (1) to approximate images as precisely as possible with evolved indirect encodings, (2) to produce visually appealing results and novel artistic styles.Comment: Proceedings of the Norwegian AI Symposium 2019 (NAIS 2019), Trondheim, Norwa

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

An Ansatz for undecidable computation in RNA-world automata

In this Ansatz we consider theoretical constructions of RNA polymers into automata, a form of computational structure. The basis for transitions in our automata are plausible RNA-world enzymes that may perform ligation or cleavage. Limited to these operations, we construct RNA automata of increasing complexity; from the Finite Automaton (RNA-FA) to the Turing Machine equivalent 2-stack PDA (RNA-2PDA) and the universal RNA-UPDA. For each automaton we show how the enzymatic reactions match the logical operations of the RNA automaton, and describe how biological exploration of the corresponding evolutionary space is facilitated by the efficient arrangement of RNA polymers into a computational structure. A critical theme of the Ansatz is the self-reference in RNA automata configurations which exploits the program-data duality but results in undecidable computation. We describe how undecidable computation is exemplified in the self-referential Liar paradox that places a boundary on a logical system, and by construction, any RNA automata. We argue that an expansion of the evolutionary space for RNA-2PDA automata can be interpreted as a hierarchical resolution of the undecidable computation by a meta-system (akin to Turing's oracle), in a continual process analogous to Turing's ordinal logics and Post's extensible recursively generated logics. On this basis, we put forward the hypothesis that the resolution of undecidable configurations in RNA-world automata represents a mechanism for novelty generation in the evolutionary space, and propose avenues for future investigation of biological automata

An investigation into Cellular Automata: The Self-Modifying Instruction-Based Approach

In this thesis we investigate a method for genotype representation in cellular automata. This method is inspired from gene regulation process in biology and is called self-modification. This is then combined with instruction-based approach to form SMIBA. In order to test this new method, SMIBA together with IBA and TT was tested on a number of problems relevant in artificial life. This firstly, being the problems of replication and of development, which are seen as vital for selfreplicating machines. Secondly, these two problems of replication and development are then combined into a new novel problem, which is then subsequently used to test the different methods. SMIBA was seen to perform well, in comparison to the other methods, on all problems tested. SMIBA and IBA were also shown to scale exceptionally well when incrementing maximum possible states of the CA, often even performing better. Further properties in SMIBA of delayed development and hierarchy were also identified

Biomimetic Engineering

Humankind is a privileged animal species for many reasons. A remarkable one is its ability to conceive and manufacture objects. Human industry is indeed leading the various winning strategies (along with language and culture) that has permitted this primate to extraordinarily increase its life expectancy and proliferation rate. (It is indeed so successful, that it now threatens the whole planet.) The design of this industry kicks off in the brain, a computing machine particularly good at storing, recognizing and associating patterns. Even in a time when human beings tend to populate non-natural, man-made environments, the many forms, colorings, textures and behaviors of nature continuously excite our senses and blend in our thoughts, even more deeply during childhood. Then, it would be exaggerated to say that Biomimetics is a brand new strategy. As long as human creation is based on previously acquired knowledge and experiences, it is not surprising that engineering, the arts, and any form of expression, is influenced by nature’s way to some extent. The design of human industry has evolved from very simple tools, to complex engineering devices. Nature has always provided us with a rich catalog of excellent materials and inspiring designs. Now, equipped with new machinery and techniques, we look again at Nature. We aim at mimicking not only its best products, but also its design principles. Organic life, as we know it, is indeed a vast pool of diversity. Living matter inhabits almost every corner of the terrestrial ecosphere. From warm open-air ecosystems to the extreme conditions of hot salt ponds, living cells have found ways to metabolize the sources of energy, and get organized in complex organisms of specialized tissues and organs that adapt themselves to the environment, and can modify the environment to their own needs as well. Life on Earth has evolved such a diverse portfolio of species that the number of designs, mechanisms and strategies that can actually be abstracted is astonishing. As August Krogh put it: "For a large number of problems there will be some animal of choice, on which it can be most conveniently studied". The scientific method starts with a meticulous observation of natural phenomena, and humans are particularly good at that game. In principle, the aim of science is to understand the physical world, but an observer’s mind can behave either as an engineer or as a scientist. The minute examination of the many living forms that surround us has led to the understanding of new organizational principles, some of which can be imported in our production processes. In practice, bio-inspiration can arise at very different levels of observation: be it social organization, the shape of an organism, the structure and functioning of organs, tissular composition, cellular form and behavior, or the detailed structure of molecules. Our direct experience of the wide portfolio of species found in nature, and their particular organs, have clearly favored that the initial models would come from the organism and organ levels. But the development of new techniques (on one hand to observe the micro- and nanostructure of living beings, and on the other to simulate the complex behavior of social communities) have significantly extended the domain of interest

An exploration and validation of computer modeling of evolution, natural selection, and evolutionary biology with cellular automata for secondary students.

The Evolutionary Tool Kit, a new software package, is the prototype of a concept simulator providing an environment for students to create microworlds of populations of artificial organisms. Its function is to model processes, concepts and arguments in natural selection and evolutionary biology, using either Mendelian asexual or sexual reproduction, or counterfactual systems such as \u27paint pot\u27 or blending inheritance. In this environment students can explore a conceptual What if? in evolutionary biology, test misconceptions and deepen understanding of inheritance and changes in populations. Populations can be defined either with typological, or with populational thinking, to inquire into the role and necessity of variation in natural selection. The approach is generative not tutorial. The interface is highly graphic with twenty traits set as icons that are moved onto the \u27phenotypes\u27. Activities include investigations of evolutionary theory of aging, reproductive advantage, sexual selection and mimicry. Design of the activities incorporates Howard Gardner\u27s Theory of Multiple Intelligences. Draft of a teacher and student manual are included

Artificial Neurogenesis: An Introduction and Selective Review

International audienceIn this introduction and review—like in the book which follows—we explore the hypothesis that adaptive growth is a means of producing brain-like machines. The emulation of neural development can incorporate desirable characteristics of natural neural systems into engineered designs. The introduction begins with a review of neural development and neural models. Next, artificial development— the use of a developmentally-inspired stage in engineering design—is introduced. Several strategies for performing this " meta-design " for artificial neural systems are reviewed. This work is divided into three main categories: bio-inspired representations ; developmental systems; and epigenetic simulations. Several specific network biases and their benefits to neural network design are identified in these contexts. In particular, several recent studies show a strong synergy, sometimes interchange-ability, between developmental and epigenetic processes—a topic that has remained largely under-explored in the literature

Using Cellular Automata and Lattice Boltzmann Methods to Model Cancer Growth: Analysis of Combination Treatment Outcomes

In Canada it is estimated that 76,600 people will die of cancer in 2014. Cancer, a collection of over 200 diseases, has differences existing between globally, between individuals and overtime in one individual. Treatment options are similarly varied. These differences make selecting the best possible treatment for every type of cancer very challenging. In addition, with no single cure for cancer, treatments are often combined in different ways to form the best overall option. In an attempt to synthesize the properties of these diseases into a collection of common cellular changes, Hanahan and Weinberg proposed ``the hallmarks of cancer -- 10 differences between healthy cells and cancer cells, present in almost every cancer. There exists the potential for treatments that are broadly applicable if they reverse these general properties. This work seeks to simulate early cancer growth, specifically looking at these hallmarks, and detect the best combinations of hallmarks to remove in order to stop cancer growth. This hybrid simulation combines a discrete model of cancer cells using cellular automata, with a continuous model of blood flow using lattice Boltzmann methods. Hallmarks relevant during the early growth stages of solid tumour development are simulated using rules in the cellular automata. Hallmarks were removed in pairs, triplets and quadruplets in order to model combination therapy, abstracting drugs that target these properties as the removal of the hallmark from the system. Overall growth of the tumours with ``treatments applied were compared to tumours where all hallmarks were present. It was found that many combinations had no effect on tumour growth. In some cases combinations even increased growth, selecting for the most aggressive hallmarks since weaker hallmarks were unavailable. However, in general, as more treatments were applied, cancer growth decreased. This work is the first to simulate removing hallmarks in pairs, triplets and quadruplets from a model with biologically relevant oxygen flow. It provides a proof of concept that not all combinations are equally effective, even if the individual treatments are effective. This work suggests some combinations should be avoided while others could potentially be beneficial in a variety of diseases