Evolving Distributed Algorithms with Genetic Programming

In this paper, we present a detailed analysis of the applica-tion of Genetic Programming to the evolution of distributed algorithms. This research field has many facets which make it especially difficult. These aspects are discussed and coun-termeasures are provided. Six different Genetic Program-ming approaches (SGP, eSGP, LGP, RBGP, eRBGP, and Fraglets) are applied to the election problem as case study utilizing these countermeasures. The results of the experiments are analyzed statistically and discussed thoroughly. Categories and Subject Descriptor