Evolution-in-materio: Solving function optimization problems using materials

Abstract

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.

Evolution-in-materio: A frequency classifier using materials

“…In other work using Mecobo it has been shown that digital logic functions can be implemented [9]. We have also obtaining encouraging results on function optimization [13], bin packing, traveling salesman [4] and machine learning classification [14] problems. In principle, some evolved systems could act as standalone devices.…”

“…The remaining members of the population are formed by mutating the parent. This algorithm was used partly because of its simplicity and partly because in other studies comparisons have been made between an evolutionary software approach and evolution-in-materio [4], [14], [13].…”

Mecobo: A Hardware and Software Platform for In Materio Evolution

“…The platform is in use by several researchers in the Nascense project consortium, e.g. University of York [13] for function optimization.…”

Evolution in Nanomaterio: The NASCENCE Project

“…Function Optimisation a. This has no inputs and as many outputs as there are dimensions in the function to be optimised [41,42] 5. Bin-Packing a.…”