Abstract
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.
Highlights
IntroductionNatural evolution has caused the construction of ‘‘machines’’ of incredible sophistication and complexity
Despite having no designer, natural evolution has caused the construction of ‘‘machines’’ of incredible sophistication and complexity
We describe a form of genetic programming known as Cartesian genetic programming(CGP) (Miller 2011; Miller and Thomson 2000)
Summary
Natural evolution has caused the construction of ‘‘machines’’ of incredible sophistication and complexity. It is possible to emulate natural evolution’s ability to utilise and exploit unknown physical and software interactions to create useful artefacts. We refer to this as computational alchemy. Miller and Hartmann suggested including a certain degree of randomness in models of logic gates and showed that this conferred a number of advantages such as fault tolerance and small circuit size (Miller and Hartmann 2001) Another possible route to enriching the basic components used in circuit evolution might be to represent the circuits at the transistor level (Greenwood and Tyrrell 2007; Trefzer and Tyrrell 2015).
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