Through the Labyrinth Evolution Finds a Way: A Silicon Ridge

Abstract. Artificial evolution is discussed in the context of a successful experiment evolving a hardware configuration for a silicon chip (a Field Programmable Gate Array); the real chip was used to evaluate individual configurations on a tone-recognition task. The evolutionary pathway is analysed; it is shown that the population is genetically highly converged and travels far through genotype space. Species Adaptation Genetic Algorithms (SAGA) are appropriate for this type of evolution, and it is shown how an appropriate mutation rate was chosen. The role of junk on the genotype is discussed, and it is suggested that neutral networks (paths through genotype space via mutations which leave fitness unchanged) may be crucial to the effectiveness of evolution. 1 Introduction To evolve silicon hardware successfully one must understand the limitations and possibilities of hardware; but one must also understand the limitations and possibilities of artificial evolution. This paper focuses on these latter concerns, and illustrates them with data taken from the first ever successful experiment evolving circuit designs directly onto a Field Programmable Gate Array for the purpose of signal recognition, using intrinsic evolution