Routing Physarum with electrical flow/current

Plasmodium stage of Physarum polycephalum behaves as a distributed dynamical pattern formation mechanism who's foraging and migration is influenced by local stimuli from a wide range of attractants and repellents. Complex protoplasmic tube network structures are formed as a result, which serve as efficient `circuits' by which nutrients are distributed to all parts of the organism. We investigate whether this `bottom-up' circuit routing method may be harnessed in a controllable manner as a possible alternative to conventional template-based circuit design. We interfaced the plasmodium of Physarum polycephalum to the planar surface of the spatially represented computing device, (Mills' Extended Analog Computer, or EAC), implemented as a sheet of analog computing material whose behaviour is input and read by a regular 5x5 array of electrodes. We presented a pattern of current distribution to the array and found that we were able to select the directional migration of the plasmodium growth front by exploiting plasmodium electro-taxis towards current sinks. We utilised this directional guidance phenomenon to route the plasmodium across its habitat and were able to guide the migration around obstacles represented by repellent current sources. We replicated these findings in a collective particle model of Physarum polycephalum which suggests further methods to orient, route, confine and release the plasmodium using spatial patterns of current sources and sinks. These findings demonstrate proof of concept in the low-level dynamical routing for biologically implemented circuit design

Evolving more efficient digital circuits by allowing circuit layout evolution and multi-objective fitness

We use evolutionary search to design combinational logic circuits. The technique is based on evolving the functionality and connectivity of a rectangular array of logic cells whose dimension is defined by the circuit layout. The main idea of this approach is to improve quality of the circuits evolved by the GA by reducing the number of active gates used. We accomplish this by combining two ideas: 1) using multi-objective fitness function; 2) evolving circuit layout. It will be shown that using these two approaches allows us to increase the quality of evolved circuits. The circuits are evolved in two phases. Initially the genome fitness in given by the percentage of output bits that are correct. Once 100% functional circuits have been evolved, the number of gates actually used in the circuit is taken into account in the fitness function. This allows us to evolve circuits with 100% functionality and minimise the number of active gates in circuit structure. The population is initialised with heterogeneous circuit layouts and the circuit layout is allowed to vary during the evolutionary process. Evolving the circuit layout together with the function is one of the distinctive features of proposed approach. The experimental results show that allowing the circuit layout to be flexible is useful when we want to evolve circuits with the smallest number of gates used. We find that it is better to use a fixed circuit layout when the objective is to achieve the highest number of 100% functional circuits. The two-fitness strategy is most effective when we allow a large number of generations

Evolution of Electronic Circuits using Carbon Nanotube Composites

Evolution-in-materio concerns the computer controlled manipulation of material systems using external stimuli to train or evolve the material to perform a useful function. In this paper we demonstrate the evolution of a disordered composite material, using voltages as the external stimuli, into a form where a simple computational problem can be solved. The material consists of single-walled carbon nanotubes suspended in liquid crystal; the nanotubes act as a conductive network, with the liquid crystal providing a host medium to allow the conductive network to reorganise when voltages are applied. We show that the application of electric fields under computer control results in a significant change in the material morphology, favouring the solution to a classification task

The genetic algorithm as a discovery engine: Strange circuits and new principles

This paper examines the idea of a genetic or evolutionary algorithm being an inspirational or discovery engine. This is illustrated in the particular context of designing electronic circuits. We argue that by connecting pieces of logic together and testing them to see if they carry out the desired function it may be possible to discover new principles of design, and new algebraic techniques. This is illustrated in the design of binary circuits, particularly arithmetic functions, where we demonstrate that by evolving a hierarchical series of examples, it becomes possible to re-discover the well known ripple-carry principle for building adder circuits of any size. We also examine the much harder case of multiplication. We show also that extending the work into the field of multiple-valued logic, the genetic algorithm is able to produce fully working circuits that lie outside conventional algebra. In addition we look at the issue of principle extraction from evolved data

Evolutionary Fabrication: An Autonomous System of Invention

Evolutionary algorithms have had success in designing complex objects, ranging from antennae used in NASA\u27s Space Technology 5 mission to astronomical telescope lenses. However, evolutionary design is limited by the ability of a simulation to accurately represent the physical world. Addition-ally, evolved designs may be well described, but they carry no set of speci˝c instructions describing how to physically create such a design. Evolutionary Fabrication (EvoFab) recti˝es this: EvoFab is a machine built upon a process that can, in principle, automatically invent and build anything, from soft robots to new toys, by evolving the process, not the product. We have designed EvoFab, which consists of four components: A) a genotype for printing objects, consisting of a linear set of instructions sent to a Fab@Home, an open-source 3D printer; B) a way to evaluate printed objects using custom machine vision algorithms; C) a way to automate printing by implementing a cus-tom conveyor belt; D) a way of elaborating upon designs by implementing a genetic algorithm. In the near term, we aim to produce an evolved arch. Current results indicate increased ˝tness over time. Future improvements are possible through restrictions in extrusion along the Y-axis as well as re˝ning ˝tness evaluation to be less exploitable

Logic matter : digital logic as heuristics for physical self-guided-assembly

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Architecture; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 123-124).Given the increasing complexity of the physical structures surrounding our everyday environment -- buildings, machines, computers and almost every other physical object that humans interact with -- the processes of assembling these complex structures are inevitably caught in a battle of time, complexity and human/machine processing power. If we are to keep up with this exponential growth in construction complexity we need to develop automated assembly logic embedded within our material parts to aid in construction. In this thesis I introduce Logic Matter as a system of passive mechanical digital logic modules for self-guided-assembly of large-scale structures. As opposed to current systems in self-reconfigurable robotics, Logic Matter introduces scalability, robustness, redundancy and local heuristics to achieve passive assembly. I propose a mechanical module that implements digital NAND logic as an effective tool for encoding local and global assembly sequences. I then show a physical prototype that successfully demonstrates the described mechanics, encoded information and passive self-guided-assembly. Finally, I show exciting potentials of Logic Matter as a new system of computing with applications in space/volume filling, surface construction, and 3D circuit assembly.by Skylar J.E. Tibbits.S.M

Generalized disjunction decomposition for evolvable hardware

Evolvable hardware (EHW) refers to self-reconfiguration hardware design, where the configuration is under the control of an evolutionary algorithm (EA). One of the main difficulties in using EHW to solve real-world problems is scalability, which limits the size of the circuit that may be evolved. This paper outlines a new type of decomposition strategy for EHW, the “generalized disjunction decomposition” (GDD), which allows the evolution of large circuits. The proposed method has been extensively tested, not only with multipliers and parity bit problems traditionally used in the EHW community, but also with logic circuits taken from the Microelectronics Center of North Carolina (MCNC) benchmark library and randomly generated circuits. In order to achieve statistically relevant results, each analyzed logic circuit has been evolved 100 times, and the average of these results is presented and compared with other EHW techniques. This approach is necessary because of the probabilistic nature of EA; the same logic circuit may not be solved in the same way if tested several times. The proposed method has been examined in an extrinsic EHW system using the$(1 + lambda)$evolution strategy. The results obtained demonstrate that GDD significantly improves the evolution of logic circuits in terms of the number of generations, reduces computational time as it is able to reduce the required time for a single iteration of the EA, and enables the evolution of larger circuits never before evolved. In addition to the proposed method, a short overview of EHW systems together with the most recent applications in electrical circuit design is provided

Morphological Evolution of Physical Robots through Model-Free Phenotype Development.

Artificial evolution of physical systems is a stochastic optimization method in which physical machines are iteratively adapted to a target function. The key for a meaningful design optimization is the capability to build variations of physical machines through the course of the evolutionary process. The optimization in turn no longer relies on complex physics models that are prone to the reality gap, a mismatch between simulated and real-world behavior. We report model-free development and evaluation of phenotypes in the artificial evolution of physical systems, in which a mother robot autonomously designs and assembles locomotion agents. The locomotion agents are automatically placed in the testing environment and their locomotion behavior is analyzed in the real world. This feedback is used for the design of the next iteration. Through experiments with a total of 500 autonomously built locomotion agents, this article shows diversification of morphology and behavior of physical robots for the improvement of functionality with limited resources.This study was supported by the Swiss National Science Foundation Grant No. PP00P2123387/1 and the ETH Zurich Research Grant ETH-23-10-3. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This is the final version of the article. It first appeared from [publisher] via http://dx.doi.org/1371/journal.pone.012844