The first time we presented the Sandpile Mutation was in GECCO (Atlanta, USA), in 2008. However, that version had some limitations that in the past couple of years we have tried to overcome. The first results of this new and improved version of the Sandpile Mutation, a Self-Organized Criticality (SOC) operator for Genetic Algorithms specifically designed for dynamic optimization, will be soon available in the LNCS volume that gathers all the contributions to the Learning and Intelligent Optimization congress, held last week in Rome. In summary, this operator uses SOC systems’ abillity to completely or partly reorganised a system after a disturbance, and evolves a varying mutation rate with a particular distribution that overcomes some of the difficulties found in dynamic optimization problems.
Here is the abstract and the presentation.
This paper describes an alternative mutation control scheme for Genetic Algorithms (GAs) inspired by the Self-Organized Criticality (SOC) theory. The strategy, which mimics a SOC system known as sandpile, is able to generate mutation rates that, unlike those generated by other methods of adaptive parameter control, oscillate between very low values and cataclysmic mutations. In order to attain the desired behaviour, the sandpile is not just attached to a GA; it is also modified in order for its conduct to reflect the stage of the search, i.e., the fitness distribution of the population. Due to its characteristics, the sandpile mutation arises as a promising candidate for efficient and yet simple and context-independent approach to dynamic optimization. An experimental study confirms this assumption: a GA with sandpile mutation outperforms a recently proposed SOC-based GA for dynamic optimization. Furthermore, the proposed method does not increase traditional GAs’ parameter set.