No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Evolution-in-materio: Solving function optimization problems using materials
Abstract
Evolution-in-materio (EIM) is a method that uses artificial evolution to exploit properties of materials to solve computational problems without requiring a detailed understanding of such properties. In this paper, we show that using a purpose-built hardware platform called Mecobo, it is possible to evolve voltages and signals applied to physical materials to solve computational problems. We demonstrate for the first time that this methodology can be applied to function optimization. We evaluate the approach on 23 function optimization benchmarks and in some cases results come very close to the global optimum or even surpass those provided by a well-known software-based evolutionary approach. This indicates that EIM has promise and further investigations would be fruitful.
... Tone discriminator: this has the same number of inputs and outputs as the frequency classifier [49] 4. Function Optimisation a. This has no inputs and as many outputs as there are dimensions in the function to be optimised [48,49] 5. Bin-Packing a. This has no inputs and as many outputs as there are items to be placed into bins [47] 6. Robot control a. ...
... The evolution-in-materio technique was also used to help find the minima of complex multi-dimensional mathematical functions [48,49]. These kinds of problems are well-known in the research field of evolutionary computation and indeed there are extensive benchmark suites containing highly nonlinear multi-modal optimisation functions. ...
... In this case, using only DC voltages as configuration parameters, the material behaves as a network of resistors. It must be noted that each sample of nanotube material contains a wide variety of such networks and the different configuration signals allow the selection of suitable networks to solve a wide variety of problems [9], [47], [48], [43]. Another model that captures the behavior of CNT materials under the influence of square waves has been proposed in [35]. ...
Natural evolution has been manipulating the properties of proteins for billions of years. This ‘design process’ is completely different to conventional human design which assembles well-understood smaller parts in a highly principled way. In evolution-in-materio (EIM), researchers use evolutionary algorithms to define configurations and magnitudes of physical variables (e.g. voltages) which are applied to material systems so that they carry out useful computation. One of the advantages of this is that artificial evolution can exploit physical effects that are either too complex to understand or hitherto unknown. An EU funded project in Unconventional Computation called NASCENCE: Nanoscale Engineering of Novel Computation using Evolution, has the aim to model, understand and exploit the behaviour of evolved configurations of nanosystems (e.g. networks of nanoparticles, carbon nanotubes, liquid crystals) to solve computational problems. The project showed that it is possible to use materials to help find solutions to a number of well-known computational problems (e.g. TSP, Bin-packing, Logic gates, etc.).
... Finally, a recent and surprising application of lateral thinking in the materials evolution space was reported by Mohid et al. 80 They described evolution-in-materio (EIM), where the usual paradigm is reversed and artificial evolution uses properties of materials to solve computational problems without requiring a detailed understanding of such properties. In the proof-ofconcept experiment the material was a mixture of carbon nanotubes and poly(methyl methacrylate) (PMMA). ...
... Two-dimensional function optimization problem for f 8 . Reproduced with permission from Mohid et al.80 Copyright 2014 IEEE. ...
Materials science is undergoing a revolution, generating valuable new materials such as flexible solar panels, biomaterials and printable tissues, new catalysts, polymers, and porous materials with unprecedented properties. However, the number of potentially accessible materials is immense. Artificial evolutionary methods such as genetic algorithms, which explore large, complex search spaces very efficiently, can be applied to the identification and optimization of novel materials more rapidly than by physical experiments alone. Machine learning models can augment experimental measurements of materials fitness to accelerate identification of useful and novel materials in vast materials composition or property spaces. This review discusses the problems of large materials spaces, the types of evolutionary algorithms employed to identify or optimize materials, and how materials can be represented mathematically as genomes, describes fitness landscapes and mutation operators commonly employed in materials evolution, and provides a comprehensive summary of published research on the use of evolutionary methods to generate new catalysts, phosphors, and a range of other materials. The review identifies the potential for evolutionary methods to revolutionize a wide range of manufacturing, medical, and materials based industries.
... (CNTs) / polymer have shown promising for the solution of Travelling Salesman (Clegg et al., 2014), logic gates (Kotsialos et al., 2014), and function optimization problems (Mohid et al., 2014). To solve problems, the material is required to hold physical richness (Miller et al., 2014 ) under a certain manipulation scheme. ...
... How should we view the results in (), (Mohid et al., 2014) in which square waves are used as one of the configuration parameters then? The only observable property of the material pinpointed thus far is the fact that it will act as low pass/high pass signal filter. ...
... Within a European project called NASCENCE (Broersma et al. 2012) it was shown that many computational problems could be solved using EIM working with carbon nanotubes: classification (Mohid et al. 2014d (Dale et al. 2016a, b, c). ...
Modern computers allow a methodical search of possibly billions of experiments and the exploitation of interactions that are not known in advance. This enables a bottom-up process of design by assembling or configuring systems and testing the degree to which they fulfill the desired goal. We give two detailed examples of this process. One is referred to as Cartesian genetic programming and the other evolution-in-materio. In the former, evolutionary algorithms are used to exploit the interactions of software components representing mathematical, logical, or computational elements. In the latter, evolutionary algorithms are used to manipulate physical systems particularly at the electrical or electronic level. We compare and contrast both approaches and discuss possible new research directions by borrowing ideas from one and using them in the other.
... shown promising for the solution of Travelling Salesman [4], logic gates [10], and function optimization problems [19]. Input and configuration signals of different kinds have been used, e.g. ...
This chapter describes some of the work carried out by members of the NASCENCE project, an FP7 project sponsored by the European Community. After some historical notes and background material, the chapter explains how nanoscale material systems have been configured to perform computational tasks by finding appropriate configuration signals using artificial evolution. Most of this exposition is centred around the work that has been carried out at the MESA+ Institute for Nanotechnology at the University of Twente using disordered networks of nanoparticles. The interested reader will also find many pointers to references that contain more details on work that has been carried out by other members of the NASCENCE consortium on composite materials based on single-walled carbon nanotubes.
Evolution-In-Materio, an unconventional computing paradigm exploiting physical properties of materials for achieving computations, is addressed here as a system which exhibits dynamical hierarchies. A description of computations is provided to show that computations within Evolution-In-Materio systems arise from the dynamics at different hierarchical levels. An information theoretic approach to formalising the notion of dynamical hierarchies is used. The approach is based on the descriptions of the system at different hierarchical levels. The concrete material addressed in this paper is a carbon nanotube / polymer nanocomposite. The choice of material is based on previous work motivated by a number of experiments conducted on such material samples. Presented findings are valuable for several reasons: better understanding of computations within Evolution-In-Materio systems, useful hints to modelling this kind of unconventional computations, useful ideas for further development of similar unconventional computing systems such as using quantum properties of charge carriers within the material and the magnetic field for guiding the search for the solution of the computational problem at hand.
Natural Evolution has been exploiting the physical properties of matter since life first appeared on earth. Evolution-in-materio (EIM) attempts to program matter so that computational problems can be solved. The beauty of this approach is that artificial evolution may be able to utilize unknown physical effects to solve computational problems. This methodology is currently being undertaken in a European research project called NASCENCE: Nanoscale Engineering for Novel Computation using Evolution. In this project, a variety of solutions to computational problems have been evolved using mixtures of carbon nanotubes and polymers at room temperature and also with gold nanoparticles at temperatures less than one Kelvin.
In function optimization one tries to find a vector of real numbers that optimizes a complex multi-modal fitness function. Although evolutionary algorithms have been used extensively to solve such problems, genetic programming has not. In this paper, we show how Cartesian Genetic Programming can be readily applied to such problems. The technique can successfully find many optima in a standard suite of benchmark functions. The work opens up new avenues of research in the application of genetic programming and also offers an extensive set of highly developed benchmarks that could be used to compare the effectiveness of different GP methodologies.
Evolution-in-materio (EIM) is the manipulation of a physical system by computer controlled evolution (CCE). It takes the position that to obtain useful functions from a physical system one needs to apply highly specific physical signals and place the system in a particular physical state. It argues that CCE is an effective methodology for doing this. One of the potential advantages of this is that artificial evolution can potentially exploit physical effects that are either too complex to understand or hitherto unknown. EIM is most commonly used as a methodology for implementing computation in physical systems. The method is a hybrid of analogue and classical computation in that it uses classical computers to program physical systems or analogue devices. Thus far EIM has only been attempted in a rather limited set of physical and chemical systems. This review paper examines past work related to EIM and discusses historical underpinnings behind such work. It describes latest developments, gives an analysis of the advantages and disadvantages of such work and the challenges that still remain.
Intrinsic evolution has been shown to be capable of exploit- ing the physical properties of materials to solve problems, however most researchers have chosen to limit themselves to using standard electronic components. However, these components are human designed and inten- tionally have predictable responses, so they may not be the most suitable physical devices to use when using a stochastic search technique. In- deed allowing computer controlled evolution (CCE) to manipulate novel physical media might allow much greater scope for the discovery of un- conventional solutions. Last year the authors demonstrated, for the first time, that CCE could manipulate liquid crystal to perform computa- tional tasks (i.e frequency discrimination, robot control). In this paper, we demonstrate that it is also possible to evolve logic gates in liquid crystal.
Although intrinsic evolution has been shown to be capable of exploiting the physical properties of materials to solve problems, most researchers have chosen to limit themselves to using standard electronic components. However, it has been previously argued that because such components are human designed and intentionally have predictable responses, they may not be the most suitable medium to use when trying to get a naturally inspired search technique to solve a problem. Indeed allowing computer controlled evolution (CCE) to manipulate novel physical media can allow much greater scope for the discovery of unconventional solutions. Last year the authors demonstrated, for the first time, that CCE could manipulate liquid crystal to perform signal processing tasks (i.e frequency discrimination). In this paper we show that CCE can use liquid crystal to solve the much harder problem of controlling a robot in real time to navigate in an environment to reach an obstructed destination point.
Artificial evolution can produce bizarre circuits that work - but do we need to understand them?
Thesis (Ph. D.)--University of Sussex, 1996.
Intrinsic evolution in evolvable hardware research has hitherto been limited to using standard electronic components as the media for problem solving. However, recently it has been argued that because such components are human designed and intentionally has predictable responses; they may not be the optimal medium to use when trying to get a naturally inspired search technique to solve a problem. Evolution has been demonstrated as capable of exploiting the physical properties of material to form solutions; however, by giving evolution only conventional components, we may be placing arbitrary constraints on our ability to solve certain problems. We have shown for the first time, that liquid crystal can be used as the physical substrate for evolution. We demonstrate that it is possible to evolve various functions, including a tone discriminator, in materio.
It is argued that natural evolution is, par excellence, an algorithm that exploits the physical properties of materials. Such an exploitation of the physical characteristics has already been demonstrated in intrinsic evolution of electronic circuits. This paper is an attempt to point the way toward the exciting possibility of using artificial evolution to directly exploit the properties of materials, possibly at a molecular level. It is suggested that this may be best accomplished in materials not normally associated with electronic functions. Electronic components have been prefected by human designers to construct circuits using the traditional top-down methodology. Workers in artificial intrinsic hardware evolution have with the best of motives, been abusing such components. It is a tribute to the amazing resourcefulness of a blind evolutionary process that it has been possible to evolve new circuits in this way. Artificial evolution may be much more effective when the configurable medium has a rich and complicated physics. This idea is discussed and particular examples that look extremely promising are given. Ultimately it may be possible to evolve entirely new technologies and new sorts of computational systems may be devised that confer many advantages over conventional electronic technology.
This paper presents a new form of Genetic Programming called Cartesian Genetic Programming in which a program is represented as an indexed graph. The graph is encoded in the form of a linear string of integers.
We describe several techniques for using bulk matter for special purpose computation. In each case it is necessary to use an evolutionary algorithm to program the substrate on which the computation is to take place. In addition, the computation comes about as a result of nearest neighbour interactions at the nano- micro- and meso-scale. In our first example we describe evolving a saw-tooth oscillator in a CMOS substrate. In the second example we demonstrate the evolution of a tone discriminator by exploiting the physics of liquid crystals. In the third example we outline using a simulated magnetic quantum dot array and an evolutionary algorithm to develop a pattern matching circuit. Another example we describe exploits the micro-scale physics of charge density waves in crystal lattices. We show that vastly different resistance values can be achieved and controlled in local regions to essentially construct a programmable array of coupled micro-scale quasiperiodic oscillators. Lastly we show an example where evolutionary algorithms could be used to control density modulations, and therefore refractive index modulations, in a fluid for optical computing.
Evolution in Materio (EIM) exploits properties of physical systems for computation. “Designs” are evolved instead of a traditional top down design approach. Computation is a product of the state(s) of the material and input data. Evolution manipulates physical processes by stimulating materials assessed in situ. A hardware-software platform designed for EIM experimentation is presented. The platform, with features designed especially for EIM, is described together with demonstration experiments using carbon nanotubes in a thick film placed on micro-electrode arrays.
Evolution-in-materio (EIM) is a method that uses artificial evolution to exploit the properties of physical matter to solve computational problems without requiring a detailed understanding of such properties. EIM has so far been applied to very few computational problems. We show that using a purpose-built hardware platform called Mecobo, it is possible to evolve voltages and signals applied to physical materials to solve machine learning classification problems. This is the first time that EIM has been applied to such problems. We evaluate the approach on two standard datasets: Lenses and Iris. Comparing our technique with a well-known software-based evolutionary method indicates that EIM performs reasonably well. We suggest that EIM offers a promising new direction for evolutionary computation.
We report for the first time on finding shortest path solutions for the travelling salesman problem (TSP) using hybrid “in materio” computation: a technique that uses search algorithms to configure materials for computation. A single-walled carbon nanotube (SWCNT) / polymer composite material deposited on a micro-electrode array is configured using static voltages so that voltage output readings determine the path order in which to visit cities in a TSP. Our initial results suggest that the hybrid computation with the SWCNT material is able to solve small instances of the TSP as efficiently as a comparable evolutionary search algorithm performing the same computation in software. Interestingly the results indicate that the hybrid system’s search performance on TSPs scales linearly rather than exponentially on these smaller instances. This exploratory work represents the first step towards building SWCNT-based electrode arrays in parallel so that they can solve much larger problems.
McGraw and Wong (1992) described an appealing index of effect size, called CL, which measures the difference between two populations in terms of the probability that a score sampled at random from the first population will be greater than a score sampled at random from the second. McGraw and Wong introduced this "common language effect size statistic" for normal distributions and then proposed an approximate estimation for any continuous distribution. In addition, they generalized CL to the n-group case, the correlated samples case, and the discrete values case.
In the current paper a different generalization of CL, called the A measure of stochastic superiority, is proposed, which may be directly applied for any discrete or continuous variable that is at least ordinally scaled. Exact methods for point and interval estimation as well as the significance tests of the A = .5 hypothesis are provided. New generalizations ofCL are provided for the multi-group and correlated samples cases.
Computational scientists have developed algorithms inspired by natural evolution for at least 50 years. These algorithms solve optimization and design problems by building solutions that are 'more fit' relative to desired properties. However, the basic assumptions of this approach are outdated. We propose a research programme to develop a new field: computational evolution. This approach will produce algorithms that are based on current understanding of molecular and evolutionary biology and could solve previously unimaginable or intractable computational and biological problems.
Several extensions to evolutionary algorithms (EAs) and particle swarm optimization (PSO) have been suggested during the last decades offering improved performance on selected benchmark problems. Recently, another search heuristic termed differential evolution (DE) has shown superior performance in several real-world applications. In this paper, we evaluate the performance of DE, PSO, and EAs regarding their general applicability as numerical optimization techniques. The comparison is performed on a suite of 34 widely used benchmark problems. The results from our study show that DE generally outperforms the other algorithms. However, on two noisy functions, both DE and PSO were outperformed by the EA.
Evolutionary programming (EP) has been applied to many numerical and combinatorial optimisation problems successfully in recent years. One disadvantage of EP is its slow convergence to a good near optimum for some function optimisation problems. In this paper, we propose a fast EP (FEP) which uses a Cauchy instead of Gaussian mutation operator as the primary search operator. The relationship between FEP and classical EP (CEP) is similar to that between the fast simulated annealing and the classical version. Extensive empirical studies have been carried out to evaluate the performance of FEP for different function optimisation problems. Fifty runs have been conducted for each of the 23 test functions in our studies. Our experimental results show that FEP performs much better than CEP for multi-modal functions with many local minima while being comparable to CEP in performance for unimodal and multi-modal functions with only a few local minima. We emphasise in the paper that no single algorithm can be the best for all problems. What we need is to identify the relationship between an algorithm and a class of problems which are most amenable to the algorithm.
Travelling salesman problem solved 'in materio' by evolved carbon nanotube device. in
- K D Clegg
- J F Miller
- M K Massey
- M C Petty