Hardware morphogenetic developmental system

Indirect genotype to phenotype mappings in the form of developmental systems may allow better scalability to larger phenotypes in evolvable hardware. This report reviews developmental systems used in evolvable hardware and it proposes a new classifications based on hardware characteristics. It then describes a genetic encoding and developmental system called the morphogenetic system which has been designed for multi-cellular circuits. This morphogenetic system is inspired upon gene expression and cell differentiation but it focuses on efficient hardware implementation. An hardware implementation on the dynamically reconfigurable POEtic circuit is described. It uses serial arithmetics and time-multiplexing to minimize ressource use

Self-repair ability of evolved self-assembling systems in cellular automata

Self-repairing systems are those that are able to reconfigure themselves following disruptions to bring them back into a defined normal state. In this paper we explore the self-repair ability of some cellular automata-like systems, which differ from classical cellular automata by the introduction of a local diffusion process inspired by chemical signalling processes in biological development. The update rules in these systems are evolved using genetic programming to self-assemble towards a target pattern. In particular, we demonstrate that once the update rules have been evolved for self-assembly, many of those update rules also provide a self-repair ability without any additional evolutionary process aimed specifically at self-repair

Exploiting development to enhance the scalability of hardware evolution.

Evolutionary algorithms do not scale well to the large, complex circuit design problems typical of the real world. Although techniques based on traditional design decomposition have been proposed to enhance hardware evolution's scalability, they often rely on traditional domain knowledge that may not be appropriate for evolutionary search and might limit evolution's opportunity to innovate. It has been proposed that reliance on such knowledge can be avoided by introducing a model of biological development to the evolutionary algorithm, but this approach has not yet achieved its potential. Prior demonstrations of how development can enhance scalability used toy problems that are not indicative of evolving hardware. Prior attempts to apply development to hardware evolution have rarely been successful and have never explored its effect on scalability in detail. This thesis demonstrates that development can enhance scalability in hardware evolution, primarily through a statistical comparison of hardware evolution's performance with and without development using circuit design problems of various sizes. This is reinforced by proposing and demonstrating three key mechanisms that development uses to enhance scalability: the creation of modules, the reuse of modules, and the discovery of design abstractions. The thesis includes several minor contributions: hardware is evolved using a common reconfigurable architecture at a lower level of abstraction than reported elsewhere. It is shown that this can allow evolution to exploit the architecture more efficiently and perhaps search more effectively. Also the benefits of several features of developmental models are explored through the biases they impose on the evolutionary search. Features that are explored include the type of environmental context development uses and the constraints on symmetry and information transmission they impose, genetic operators that may improve the robustness of gene networks, and how development is mapped to hardware. Also performance is compared against contemporary developmental models

Bio-inspired cellular machines:towards a new electronic paper architecture

Information technology has only been around for about fifty years. Although the beginnings of automatic calculation date from as early as the 17th century (W. Schickard built the first mechanical calculator in 1623), it took the invention of the transistor by W. Shockley, J. Bardeen and W. Brattain in 1947 to catapult calculators out of the laboratory and produce the omnipresence of information and communication systems in today's world. Computers not only boast very high performance, capable of carrying out billions of operations per second, they are taking over our world, working their way into every last corner of our environment. Microprocessors are in everything, from the quartz watch to the PC via the mobile phone, the television and the credit card. Their continuing spread is very probable, and they will even be able to get into our clothes and newspapers. The incessant search for increasingly powerful, robust and intelligent systems is not only based on the improvement of technologies for the manufacture of electronic chips, but also on finding new computer architectures. One important source of inspiration for the research of new architectures is the biological world. Nature is fascinating for an engineer: what could be more robust, intelligent and able to adapt and evolve than a living organism? Out of a simple cell, equipped with its own blueprint in the form of DNA, develops a complete multi-cellular organism. The characteristics of the natural world have often been studied and imitated in the design of adaptive, robust and fault-tolerant artificial systems. The POE model resumes the three major sources of bio-inspiration: the evolution of species (P: phylogeny), the development of a multi-cellular organism by division and differentiation (O: ontogeny) and learning by interaction with the environment (E: epigenesis). This thesis aims to contribute to the ontogenetic branch of the POE model, through the study of three completely original cellular machines for which the basic element respects the six following characteristics: it is (1) reconfigurable, (2) of minimal complexity, (3) present in large numbers, (4) interconnected locally with its neighboring elements, (5) equipped with a display capacity and (6) with sensor allowing minimal interaction. Our first realization, the BioWall, is made up of a surface of 4,000 basic elements or molecules, capable of creating all cellular systems with a maximum of 160 × 25 elements. The second realization, the BioCube, transposes the two-dimensional architecture of the BioWall into a two-dimensional space, limited to 4 × 4 × 4 = 64 basic elements or spheres. It prefigures a three-dimensional computer built using nanotechnologies. The third machine, named BioTissue, uses the same hypothesis as the BioWall while pushing its performance to the limits of current technical possibilities and offering the benefits of an autonomous system. The convergence of these three realizations, studied in the context of emerging technologies, has allowed us to propose and define the computer architecture of the future: the electronic paper

Tissu numérique cellulaire à routage et configuration dynamiques

In the design of new machines or in the development of new concepts, mankind has often observed nature, looking for useful ideas and sources of inspiration. The design of electronic circuits is no exception, and a considerable number of realizations have drawn inspiration from three aspects of natural systems : the evolution of species (Phylogenesis), the development of an organism starting from a single cell (Ontogenesis), and learning, as performed by our brain (Epigenesis). These three axes, grouped under the acronym POE, have for the most part been exploited separately : evolutionary principles allow to solve problems for which it is hard to find a solution with a deterministic method, while some electronic circuits draw inspiration from healing process in living beings to achieve self-repair, and artificial neural networks have the capability to efficiently execute a wide range of tasks. At this time, no electronic tissue capable of bringing them together seems to exist. The introduction of reconfigurable circuits called Field Programmable Gate Arrays (FPGAs), whose behavior can be redefined as often as desired, made prototyping such systems easier. FPGAs, by allowing a relatively simple implementation in hardware, can considerably increase the systems' performance and are thus extensively used by researchers. However, they lack plasticity, not being able to easily modify themselves without an external intervention. This PhD thesis, developed in the framework of the European POEtic project, proposes to define a new reconfigurable electronic circuit, with the goal of supplying a new substrate for bio-inspired applications that bring all three axes into play. This circuit is mainly composed of a microprocessor and an array of reconfigurable elements, the latter having been designed during this thesis. Evolutionary processes are executed by the microprocessor, while epigenetic and ontogenetic mechanisms are applied in the reconfigurable array, to entities seen as multicellular artificial organisms. Relatively similar to current commercial FPGAs, this subsystem offers however some unique features. First, the basic elements of the array have the capability to partially reconfigure other elements. Auto-replication and differentiation mechanisms can exploit this capability to let an organism grow or to modify its behavior. Second, a distributed routing layer allows to dynamically create connections between parts of the circuit at runtime. With this feature, cells (artificial neurons, for example) implemented in the reconfigurable array can initiate new connections in order to modify the global system behavior. This distributed routing mechanism, one of the major contributions of this thesis, induced the realization of several algorithms. Based on a parallel implementation of Lee's algorithm, these algorithms are totally distributed, no global control being necessary to create new data paths. Four algorithms have been defined implemented in hardware in the form of routing units connected to 3, 4, 6, or 8 neighbors. These units are all identical and are responsible for the routing processes. An analysis of their properties allows us to define the best algorithm, coupled with the most efficient neighborhood, in terms of congestion and of the number of transistors needed for a hardware realization. We finish the routing chapter by proposing a fifth algorithm that, unlike the previous ones, is constructed only through local interactions between routing units. It offers a better scalability, at the price of increased hardware overhead. Finally, the POEtic chip, in which one of our algorithms has been implemented, has been physically realized. We present different POE mechanisms that take advantage of its new features. Among these mechanisms, we can notably cite auto-replication, evolvable hardware, developmental systems, and self-repair. All of these mechanisms have been developed with the help of a circuit simulator, also designed in the framework of this thesis

Digital control networks for virtual creatures

Robot control systems evolved with genetic algorithms traditionally take the form of floating-point neural network models. This thesis proposes that digital control systems, such as quantised neural networks and logical networks, may also be used for the task of robot control. The inspiration for this is the observation that the dynamics of discrete networks may contain cyclic attractors which generate rhythmic behaviour, and that rhythmic behaviour underlies the central pattern generators which drive lowlevel motor activity in the biological world. To investigate this a series of experiments were carried out in a simulated physically realistic 3D world. The performance of evolved controllers was evaluated on two well known control tasks—pole balancing, and locomotion of evolved morphologies. The performance of evolved digital controllers was compared to evolved floating-point neural networks. The results show that the digital implementations are competitive with floating-point designs on both of the benchmark problems. In addition, the first reported evolution from scratch of a biped walker is presented, demonstrating that when all parameters are left open to evolutionary optimisation complex behaviour can result from simple components

Gate-Level Morphogenetic Evolvable Hardware for Scalability and Adaptation on FPGAs

Traditional approaches to evolvable hardware (EHW), in which the field programmable gate array (FPGA) configuration is directly encoded, have not scaled well with increasing circuit and FPGA complexity. To overcome this there have been moves towards encoding a growth process, known as morphogenesis, however existing approaches have tended to abstract away the underlying FPGA architecture. Although currently commercially available FPGAs are not the most evolution-friendly platforms, having complex architectures and issues with potentially damaging configurations, evolving circuits on commercially available devices without requiring a move to high-level building blocks is a necessary prerequisite for the adoption of EHW to solving real problems in electronic design, repair and adaptation. In this paper we present a morphogenetic EHW model where growth is directed by the gate-level state of the FPGA. We demonstrate that this approach consistently outperforms a traditional EHW approach using a direct encoding, in the number of generations required to find an optimal solution, and in its ability to scale to increases in circuit size and complexity. Issues in EHW problem solvability are also identified, and preliminary work is presented showing that a morphogenetic approach to EHW may be well suited to correcting damaged circuits

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance