Evolution-In-Materio: Solving Computational Problems Using Materials

The motivation behind the research is to show that evolutionary algorithms can exploit properties of materials to solve various computational problems without requiring a detailed understanding of such properties. This approach is referred to as evolution-in-materio. In this research, it has been shown that using a purpose-built hardware platform called Mecobo, it is possible to evolve voltages and signals applied to physical materials to solve a number of computational problems. Here it has been demonstrated for the first time that the evolution-in-materio method can be applied to function optimisation, machine learning classification, frequency classification, even parity and bin packing problems. This evolution-in-materio method has also been applied here to discriminate tones and control robots. The physical material used in each of these experiments is a mixture of single-walled carbon nanotubes and a polymer. This is the first time that such material has been used to solve computational problems. The results of all of these experiments indicate that evolution-in-materio has promise and further investigations would be fruitful. Other than the solutions regarding these computational problems, this thesis has also devised and investigated suitable input-output mappings and input signals that allow various computational problems to be solved using the Mecobo platform and the experimental material

M+D: conceptual guidelines for compiling a materials library

This article proposes to present a study conducted by the Raw Materials research group, the results of which comprise the conceptual guidelines for compiling an M+D material library. The study includes the topic, materials and design taking the impact of the changes that came into being in the post industrial era on project methodologies and the search for information regarding materials. Taking into account the importance and complexity that these relationships have taken on currently, we have studied the issue of materials based on Manzini (1983) and Ashby and Johnson (2002). Afterward different databases and materials libraries located in the Brazil, the United States, France and Italy geared toward design professionals and students were analyzed to understand what information and means of access to them were available. The project methodologies were approached based on Löbach (1991), Bürdeck (1994), Schulmann (1994), Baxter (1998), Dantas (1998 and 2005) and Papanek (1995 and 2000). This study sought to identify the key elements of the role of materials in the project process today, to serve as a parameter for the analysis of the models studied. A comparative analysis of the models investigated enabled identification of positive and negative aspects to adapt to the needs previously mentioned and identify conceptual guidelines for compiling a collection of materials for use in design projects. Keywords: Design, Materials, Project Methodology, Library</p

M+D: conceptual guidelines for compiling a materials library

This article proposes to present a study conducted by the Raw Materials research group, the results of which comprise the conceptual guidelines for compiling an M+D material library. The study includes the topic, materials and design taking the impact of the changes that came into being in the post industrial era on project methodologies and the search for information regarding materials. Taking into account the importance and complexity that these relationships have taken on currently, we have studied the issue of materials based on Manzini (1983) and Ashby and Johnson (2002). Afterward different databases and materials libraries located in the Brazil, the United States, France and Italy geared toward design professionals and students were analyzed to understand what information and means of access to them were available. The project methodologies were approached based on Löbach (1991), Bürdeck (1994), Schulmann (1994), Baxter (1998), Dantas (1998 and 2005) and Papanek (1995 and 2000). This study sought to identify the key elements of the role of materials in the project process today, to serve as a parameter for the analysis of the models studied. A comparative analysis of the models investigated enabled identification of positive and negative aspects to adapt to the needs previously mentioned and identify conceptual guidelines for compiling a collection of materials for use in design projects. Keywords: Design, Materials, Project Methodology, Library</p

Reservoir Computing in Materio : An Evaluation of Configuration through Evolution

Recent work has shown that computational substrates made from carbon nanotube/polymer mixtures can form trainable Reservoir Computers. This new reservoir computing platform uses computer based evolutionary algorithms to optimise a set of electrical control signals to induce reservoir properties within the substrate. In the training process, evolution decides the value of analogue control signals (voltages) and the location of inputs and outputs on the substrate that improve the performance of the subsequently trained reservoir readout. Here, we evaluate the performance of evolutionary search compared to randomly assigned electrical configurations. The substrate is trained and evaluated on time-series prediction using the Santa Fe Laser generated competition data (dataset A). In addition to the main investigation, we introduce two new features closely linked to the traditional reservoir computing architecture, adding an evolvable input-weighting mechanism and a reservoir time-scaling parameter. The experimental results show evolved configurations across all four test substrates consistently produce reservoirs with greater performance than randomly configured reservoirs. The results also show that applying both input-weighting and timescaling simultaneously can provide additional tuning to the task, improving performance. For one material, the evolved reservoir is shown to outperform – for this task – all other hardwarebased reservoir computers found in the literature. The same material also outperforms a simple evolved simulated Echo State Network of the same size. The performance of this material is reported to be both consistent after long time-periods and after reconfiguration to other tasks

Harnessing Evolution in-Materio as an Unconventional Computing Resource

This thesis illustrates the use and development of physical conductive analogue systems for unconventional computing using the Evolution in-Materio (EiM) paradigm. EiM uses an Evolutionary Algorithm to configure and exploit a physical material (or medium) for computation. While EiM processors show promise, fundamental questions and scaling issues remain. Additionally, their development is hindered by slow manufacturing and physical experimentation. This work addressed these issues by implementing simulated models to speed up research efforts, followed by investigations of physically implemented novel in-materio devices. Initial work leveraged simulated conductive networks as single substrate ‘monolithic’ EiM processors, performing classification by formulating the system as an optimisation problem, solved using Differential Evolution. Different material properties and algorithm parameters were isolated and investigated; which explained the capabilities of configurable parameters and showed ideal nanomaterial choice depended upon problem complexity. Subsequently, drawing from concepts in the wider Machine Learning field, several enhancements to monolithic EiM processors were proposed and investigated. These ensured more efficient use of training data, better classification decision boundary placement, an independently optimised readout layer, and a smoother search space. Finally, scalability and performance issues were addressed by constructing in-Materio Neural Networks (iM-NNs), where several EiM processors were stacked in parallel and operated as physical realisations of Hidden Layer neurons. Greater flexibility in system implementation was achieved by re-using a single physical substrate recursively as several virtual neurons, but this sacrificed faster parallelised execution. These novel iM-NNs were first implemented using Simulated in-Materio neurons, and trained for classification as Extreme Learning Machines, which were found to outperform artificial networks of a similar size. Physical iM-NN were then implemented using a Raspberry Pi, custom Hardware Interface and Lambda Diode based Physical in-Materio neurons, which were trained successfully with neuroevolution. A more complex AutoEncoder structure was then proposed and implemented physically to perform dimensionality reduction on a handwritten digits dataset, outperforming both Principal Component Analysis and artificial AutoEncoders. This work presents an approach to exploit systems with interesting physical dynamics, and leverage them as a computational resource. Such systems could become low power, high speed, unconventional computing assets in the future

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

Potential implementation of Reservoir Computing models based on magnetic skyrmions

Reservoir Computing is a type of recursive neural network commonly used for recognizing and predicting spatio-temporal events relying on a complex hierarchy of nested feedback loops to generate a memory functionality. The Reservoir Computing paradigm does not require any knowledge of the reservoir topology or node weights for training purposes and can therefore utilize naturally existing networks formed by a wide variety of physical processes. Most efforts prior to this have focused on utilizing memristor techniques to implement recursive neural networks. This paper examines the potential of skyrmion fabrics formed in magnets with broken inversion symmetry that may provide an attractive physical instantiation for Reservoir Computing.Comment: 11 pages, 3 figure

In-Materio Extreme Learning Machines

Nanomaterial networks have been presented as a building block for unconventional in-Materio processors. Evolution in-Materio (EiM) has previously presented a way to congure and exploit physical materials for computation, but their ability to scale as datasets get larger and more complex remains unclear. Extreme Learning Machines (ELMs) seek to exploit a randomly initialised single layer feed forward neural network by training the output layer only. An analogy for a physical ELM is produced by exploiting nanomaterial networks as material neurons within the hidden layer. Circuit simulations are used to eciently investigate diode-resistor networks which act as our material neurons. These in-Materio ELMs (iM-ELMs) outperform common classication methods and traditional articial ELMs of a similar hidden layer size. For iM-ELMs using the same number of hidden layer neurons, leveraging larger more complex material neuron topologies (with more nodes/electrodes) leads to better performance, showing that these larger materials have a better capability to process data. Finally, iM-ELMs using virtual material neurons, where a single material is re-used as several virtual neurons, were found to achieve comparable results to iM-ELMs which exploited several dierent materials. However, while these Virtual iM-ELMs provide signicant exibility, they sacrice the highly parallelised nature of physically implemented iM-ELMs

Evolutionary computation based on nanocomposite training: application to data classification

Research into novel materials and computation frameworks by-passing the limitations of the current paradigm, has been identified as crucial for the development of the next generation of computing technology. Within this context, evolution in materio (EiM) proposes an approach where evolutionary algorithms (EAs) are used to explore and exploit the properties of un-configured materials until they reach a state where they can perform a computational task. Following an EiM approach, this thesis demonstrates the ability of EAs to evolve dynamic nanocomposites into data classifiers. Material-based computation is treated as an optimisation problem with a hybrid search space consisting of configuration voltages creating an electric field applied to the material, and the infinite space of possible states the material can reach in response to this field. In a first set of investigations, two different algorithms, differential evolution (DE) and particle swarm optimisation (PSO), are used to evolve single-walled carbon nanotube (SWCNT) / liquid crystal (LC) composites capable of classifying artificial, two-dimensional, binary linear and non-linear separable and merged datasets at low SWCNT concentrations. The difference in search behaviour between the two algorithms is found to affect differently the composite’ state during training, which in turn affects the accuracy, consistency and generalisation of evolved solutions. SWCNT/LC processors are also able to scale to complex, real-life classification problems. Crucially, results suggest that problem complexity influences the properties of the processors. For more complex problems, networks of SWCNT structures tend to form within the composite, creating stable devices requiring no configuration voltages to classify data, and with computational capabilities that can be recovered more than several hours after training. A method of programming the dynamic composites is demonstrated, based on the reapplication of sequences of configuration voltages which have produced good quality SWCNT/LC classifiers. A second set of investigations aims at exploiting the properties presented by the dynamic nanocomposites, whilst also providing a means for evolved device encapsulation, making their use easier in out-of-the lab applications. Novel composites based on SWCNTs dispersed in one-part UV-cure epoxies are introduced. Results obtained with these composites support their choice for use in subsequent EiM research. A final discussion is concerned with evolving an electro-biological processor and a memristive processor. Overall, the work reported in the thesis suggests that dynamic nanocomposites present a number of unexpected, potentially attractive properties not found in other materials investigated in the context of EiM