Evolution in materio: initial experiments with liquid crystal

Harding, Simon; Miller, Julian F.

doi:10.1109/eh.2004.1310844

Cited by 29 publications

(37 citation statements)

References 10 publications

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“…The material blend is drop-deposited within a nylon washer (2.5 mm internal diameter) fixed to this platform for material/electronics interaction. Different organic and inorganic media have been used as materials, such as slime moulds [7], bacterial consortia [1], cells (neurons) [18], liquid crystals (LC) panels [6] and nano-particles [3]. Single walled carbon nanotubes (SWCNT) based materials have shown the potential to solve computational problems [8,11,22,14,15].…”

Section: Introductionmentioning

confidence: 99%

Data Classification Using Carbon-Nanotubes and Evolutionary Algorithms

Vissol-Gaudin

Kotsialos

Massey

et al. 2016

Parallel Problem Solving From Nature – PPSN XIV

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The nal publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-45823-660Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract. The potential of Evolution in Materio (EiM) for machine learning problems is explored here. This technique makes use of evolutionary algorithms (EAs) to influence the processing abilities of an unconfigured physically rich medium, via exploitation of its physical properties. The EiM results reported are obtained using particle swarm optimisation (PSO) and differential evolution (DE) to exploit the complex voltage/current relationship of a mixture of single walled carbon nanotubes (SWCNTs) and liquid crystals (LCs). The computational problem considered is simple binary data classification. Results presented are consistent and reproducible. The evolutionary process based on EAs has the capacity to evolve the material to a state where data classification can be performed. Finally, it appears that through the use of smooth signal inputs, PSO produces classifiers out of the SWCNT/LC substrate which generalise better than those evolved with DE.

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Section: Introductionmentioning

confidence: 99%

Data Classification Using Carbon-Nanotubes and Evolutionary Algorithms

Vissol-Gaudin

Kotsialos

Massey

et al. 2016

Parallel Problem Solving From Nature – PPSN XIV

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“…We infer hierarchies of evolutionary complexity from computational and experimental laboratory studies of evolving of logic gates in liquid crystal [4] and excitable chemical medium [5]. See full details in the article cited, here we provide only a very brief overview of the results.…”

Section: Evolutionary Complexity Of Boolean Gatesmentioning

confidence: 99%

“…Nonlinear spatial extended media-liquid crystal [4] and Belousov-Zhabotinsky system [5]-were chosen as substrates of various logical gates' evolution because these media are possibly the best prototypes of nonclassical, unconventional, computing devices [6]. One-dimensional cellular automata were then selected as substrates to implement the gates evolved because the automata are good approximators of the real world [7], the space-time dynamic of the automata is easy to visualize, and methods for exploring complexity of the automata are well-established [8][9][10][11][12].…”

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confidence: 99%

Are complex systems hard to evolve?

Adamatzky

Bull

2009

Complexity

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Evolutionary complexity is measured here by the number of trials/evaluations needed for evolving a logical gate in a nonlinear medium. Behavioral complexity of the gates evolved is characterized in terms of cellular automata behavior. We speculate that hierarchies of behavioral and evolutionary complexities are isomorphic up to some degree, subject to substrate specificity of evolution, and the spectrum of evolution parameters.2009 Wiley Periodicals, Inc. Complexity 14: 15-20, 2009 Key Words: logic gates; evolution; cellular automata; complexity T here are many ways to measure the complexity of a system [1-3]. For example, having a problem we can estimate its complexity as the number of steps a computer takes to solve the problem (computational complexity). A descriptive complexity of a system is evaluated by the length of a minimal program/ model representing the system. Complexity of a finite state machine can be classified in sets of regular languages accepted by the machine. There are also measures based on how difficult it is to predict the evolution of a system.There are, however, few-if any-results related to the comparison of different measures of complexity. This short article aims to raise readers' awareness of the subject and to invite them to undertake research in ''comparative complexity.'' We compare the evolutionary complexity of logical functions (time required to evolve the functions) with the behavioral complexity of the functions (complexity of sequences of data iteratively processed by the functions).Nonlinear spatial extended media-liquid crystal [4] and Belousov-Zhabotinsky system [5]-were chosen as substrates of various logical gates' evolution because these media are possibly the best prototypes of nonclassical, unconventional, computing devices [6]. One-dimensional cellular automata were then selected as substrates to implement the gates evolved because the automata are good approximators of the real world [7], the space-time dynamic of the automata is easy to visualize, and methods for exploring complexity of the automata are well-established [8][9][10][11][12]. EVOLUTIONARY COMPLEXITY OF BOOLEAN GATESWe infer hierarchies of evolutionary complexity from computational and experimental laboratory studies of evolving of logic gates in liquid crystal [4] and excitable chemical medium [5]. See full details in the article cited, here we provide only a very brief overview of the results.

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“…Evolution-in-materio (EIM) aims to mimic the exploitation of physical properties by natural evolution by manipulating physical systems using computer controlled evolution (CCE) [6,10]. In particular, EIM aims to exploit the properties of physical systems for solving computational problems.…”

Section: Introductionmentioning

confidence: 99%

“…They found that computer-controlled evolution could utilize the physical properties of liquid crystal to help solve a number of computational problems [4]: -Two input logic gates: OR, AND, NOR, NAND, etc. [6]. -Tone Discriminator: A device was evolved which could differentiate different frequencies [4].…”

Section: Introductionmentioning

confidence: 99%

Evolution-In-Materio: Solving Machine Learning Classification Problems Using Materials

Mohid

Miller

Harding

et al. 2014

Parallel Problem Solving From Nature – PPSN XIII

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Abstract. 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.

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Evolution in materio: initial experiments with liquid crystal

Abstract: Intrinsic evolution is often limited to using standard electronic

Cited by 29 publications

References 10 publications

Data Classification Using Carbon-Nanotubes and Evolutionary Algorithms

Data Classification Using Carbon-Nanotubes and Evolutionary Algorithms

Are complex systems hard to evolve?

Evolution-In-Materio: Solving Machine Learning Classification Problems Using Materials

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