Analogue Circuit Control through Gene Expression

Software configurable analogue arrays offer an intriguing platform for automated design by evolutionary algorithms. Like previous evolvable hardware experiments, these platforms are subject to noise during physical interaction with their environment. We report preliminary results of an evolutionary system that uses concepts from gene expression to both discover and decide when to deploy analogue circuits. The output of a circuit is used to trigger its reconfiguration to meet changing conditions. We examine the issues of noise during our evolutionary runs, show how this was overcome and illustrate our system with a simple proof-of-concept task that shows how the same mechanism of control works for progressive developmental stages (canalisation) or adaptable control (homoeostasis). 1 Background and motivation We present a system for the automated discovery and deployment of software configured analogue circuits. High level circuit components (for example, analogue filters) are modelled as ‘genes’. Genes are ‘expressed’ in response to circuit output; as conditions change new genes are expressed, triggering the deployment of a new circuit. Gene characteristics are defined in a genome representation discovered using evolutionary algorithms. Evolutionary computation has a proven record as a technique for searchbased optimisation [1–3]. The method has also been used to automate design discovery [4, 5]. To date most work in the field focuses on the performance of evolutionary search. Fewer researchers are questioning the precepts on which stochastic, population-based algorithms are founded, whether their evolutionary model corresponds to modern biological thinking [6] or the role of developmental processes in evolution [7]. The latter is particularly important with regard to how gene expression explores the functional search space [8]. Thompson’s ground-breaking work in the mid-1990s established that search algorithms could be used to discover novel hardware configurations that lie outside conventional engineering knowledge [9]. Thompson concentrated on innovation rather than rapid discovery, and was careful to design his experiments so that the search could exploit physical characteristics not normally incorporated into an engineer’s design [10]. Following on from this, further examples using evolutionary algorithms in software configured physical media have come from