Workshops at PPSN XI

Last week in conjunction with  PPSN XI, the Self* 2010 and PARCO 2010 workshops were held in Kraków (Poland)  where we were presenting  some of our most recent works. Specifically, you can find the respective presentations below.

  • A Self-Organized Critically Online Adjustment of Genetic Algorithms’ Mutation Rate

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 sand pile, 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.

  • Influence of the Population Structure on the Performance of an Agent-based Evolutionary Algorithm

The Evolvable Agent model is a Peer-to-Peer Evolutionary Algorithm which focuses on distributed optimization over Peer-to-Peer infrastructures. The key idea of the model is that every agent-individual is designated as a peer and adopts a decentralised population structure defined by the Peer-to-Peer protocol newscast. In that context, this work aims to compare performances of the approach
when using two additional population structures other than newscast: a ring and a Watts-Strogatz topology.


One thought on “Workshops at PPSN XI

  1. Pingback: Environment-driven evolution in robot swarms « GeNeura Team

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