Evolution in materio: looking beyond the silicon box

Miller, Julian F.; Downing, Keith L.

doi:10.1109/eh.2002.1029882

Cited by 92 publications

(80 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Evolution-in-materio (EiM) is an Unconventional Computing (UC) technique which focuses on exploiting the underlying properties of materials to bring them to a computation inducing state [12]. Contrary to traditional computing with Metal-Oxide-Silicon-Field-Effect-Transistor (MOFSET) technology, where everything is designed, produced and programmed very carefully, EiM uses a bottom up approach where computation is performed by the material without having explicit knowledge of its internal properties [13].…”

Section: Introductionmentioning

confidence: 99%

“…Because of feedback provided by the iterative nature of stochastic optimisation interacting with the material, the identified solutions were based on the specific FPGA's properties that were unaccounted for during the board's design. EiM replaces the FPGAs with un-configured material systems favouring exploitation of some physical property by a search algorithm [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data Classification Using Carbon-Nanotubes and Evolutionary Algorithms

Vissol-Gaudin

Kotsialos

Massey

et al. 2016

Parallel Problem Solving From Nature – PPSN XIV

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…Evolution in Materio (EIM) [4,5,6] is such an unconventional approach where the underlying physical properties of bulk materials are explored and exploited for computation. In contrast to a traditional approach where a substrate, e.g.…”

Section: Introductionmentioning

confidence: 99%

Mecobo: A Hardware and Software Platform for In Materio Evolution

Lykkebø

Harding

Tufte

et al. 2014

Unconventional Computation and Natural Computation

View full text Add to dashboard Cite

Abstract. Evolution in Materio (EIM) exploits properties of physical systems to compute. "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 especially for EIM is described together with demonstration experiments using carbon nanotubes in a thick film placed on micro electrode arrays.

show abstract

“…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%

“…Evolution-in-materio was first described by Miller and Downing [10]. The concept was inspired by the work of Adrian Thompson who investigated whether it was possible to evolve working electronic circuits using a silicon chip called a Field Programmable Gate Array (FPGA).…”

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

View full text Add to dashboard Cite

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.

show abstract

Evolution in materio: looking beyond the silicon box

Cited by 92 publications

References 30 publications

Data Classification Using Carbon-Nanotubes and Evolutionary Algorithms

Data Classification Using Carbon-Nanotubes and Evolutionary Algorithms

Mecobo: A Hardware and Software Platform for In Materio Evolution

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

Contact Info

Product

Resources

About