Applying Self-Organised Criticality to Evolutionary Algorithms

Abstract

Complex systems are typically composed of a large number of locally interacting components that operate at a critical state between chaos and order, which is known as self-organised criticality. A common feature of this state is the exponential (power law) relationship between the frequency of an event and the size of its impact, such as the event of an earthquake and its strength on the Richter scale. Most state transitions in a component of a complex system only affect its neighbourhood, but once in a while entire avalanches of propagating state transitions can lead to a major reconfiguration of the system. In evolution, this system behaviour has been identified in species extinction on an evolutionary time-scale, where avalanches correspond to mass extinction. In this paper, we applied the concept of self-organised criticality (SOC) to control mutation on the individual level and extinction on the population level in the context of evolutionary algorithms (EA). Our results show that the SOC EAs clearly outperform standard EAs and a previously introduced mass extinction model. Interestingly, the great performance of our SOC EAs is based on a very simple modification of standard EAs and implies almost no additional computational costs.