Chemical Computing Through Simulated Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InCollection{Bull:2017:miller,

• author = "Larry Bull and Rita Toth and Chris Stone and Ben {De Lacy Costello} and Andrew Adamatzky",
• title = "Chemical Computing Through Simulated Evolution",
• booktitle = "Inspired by Nature: Essays Presented to Julian F. Miller on the Occasion of his 60th Birthday",
• publisher = "Springer",
• year = "2017",
• editor = "Susan Stepney and Andrew Adamatzky",
• volume = "28",
• series = "Emergence, Complexity and Computation",
• chapter = "13",
• pages = "269--286",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-319-67996-9",
• DOI = "doi:10.1007/978-3-319-67997-6_13",
• abstract = "Many forms of unconventional computing, i.e., massively parallel computers which exploit the non-linear material properties of their substrate, can be realised through simulated evolution. That is, the behaviour of non-linear media can be controlled automatically and the structural design of the media optimized through the nature-inspired machine learning approach. This chapter describes work using the Belousov-Zhabotinsky reaction as a non-linear chemical medium in which to realise computation. Firstly, aspects of the basic structure of an experimental chemical computer are evolved to implement two Boolean logic functions through a collision-based scheme. Secondly, a controller is evolved to dynamically affect the rich spatio-temporal chemical wave behaviour to implement three Boolean functions, in both simulation and experimentation.",
• notes = "part of \cite{miller60book} https://link.springer.com/bookseries/10624",

}

Genetic Programming entries for Larry Bull Rita Toth Chris Stone Ben De Lacy Costello Andrew Adamatzky

Citations