Experiments on the Automatic Evolution of Protocols using Genetic Programming

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Experiments on the Automatic Evolution of Protocols using Genetic Programming (2005)

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by Lidia Yamamoto , Christian Tschudin

Venue:	Computer Science Department, University of Basel, Switzerland. Her
Citations:	19 - 4 self

BibTeX

@INPROCEEDINGS{Yamamoto05experimentson,
    author = {Lidia Yamamoto and Christian Tschudin},
    title = {Experiments on the Automatic Evolution of Protocols using Genetic Programming},
    booktitle = {Computer Science Department, University of Basel, Switzerland. Her},
    year = {2005},
    pages = {13--28},
    publisher = {Springer}
}

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Abstract

One of the biggest challenges in obtaining truly autonomic, selfmanaged networks is to automate the process of software evolution, and in particular, the evolution of protocol implementations and configurations. Such networks ultimately require self-modifying, evolving protocol software. Otherwise humans must intervene in every situation that has not been anticipated at design time. For this to become feasible autonomic systems must ensure non-disruptive, resilient on-line software evolution. We are starting to explore approaches to network evolution that operate directly at the code level. We investigate related code steering techniques in two directions: One is the fully automatic selection of protocol service elements where, depending on device characteristics and current operation environment, each communication entity has to select among a potentially wide variety of protocol implementations providing similar services. The other direction relates to the automatic synthesis of new protocol elements which are the result of optimizing existing implementations for a specific context. In both cases we look at genetic programming as a tool to generate new code and software configurations automatically. We propose a framework for such a resilient protocol evolution and report on first exploratory results on the adaptation and re-adaptation to environmental conditions, and the elimination of superfluous code.

Keyphrases

genetic programming automatic evolution protocol implementation environmental condition new protocol element resilient protocol evolution design time code level software configuration network evolution communication entity resilient on-line software evolution specific context current operation environment automatic synthesis protocol software device characteristic new code first exploratory result protocol service element feasible autonomic system related code superfluous code similar service selfmanaged network automatic selection wide variety software evolution

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