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@InProceedings{Coker/2015,
AUTHOR = {Coker, Zack and Garlan, David and Le Goues, Claire},
TITLE = {SASS: Self-adaptation using stochastic search},
YEAR = {2015},
MONTH = {18-19 May},
BOOKTITLE = {Proceedings 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2015)},
PDF = {http://acme.able.cs.cmu.edu/pubs/uploads/pdf/PID3582925.pdf},
ABSTRACT = {Future-generation self-adaptive systems will need
to be able to optimize for multiple interrelated, difficult-to-measure,
and evolving quality properties. To navigate this complex
search space, current self-adaptive planning techniques
need to be improved. In this position paper, we argue that the
research community should more directly pursue the application
of stochastic search techniques—search techniques, such as hill
climbing or genetic algorithms, that incorporate an element of
randomness—to self-adaptive systems research. These techniques
are well-suited to handling multi-dimensional search spaces and
complex problems, situations which arise often for self-adaptive
systems. We believe that recent advances in both fields make this
a particularly promising research trajectory. We demonstrate
one way to apply some of these advances in a search-based
planning prototype technique to illustrate both the feasibility and
the potential of the proposed research. This strategy informs
a number of potentially interesting research directions and
problems. In the long term, this general technique could enable
sophisticated plan generation techniques that improve domain
specific knowledge, decrease human effort, and increase the
application of self-adaptive systems.},
KEYWORDS = {Self-adaptation}
}
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