  Autonomic management is aimed at adapting to uncertainty. Hence, it is
devised as m-connected k-dominating set problem, resembled by dominator and
dominate, such that dominators are resilient up to m-1 uncertainty among them
and dominate are resilient up to k-1 uncertainty on their way to dominators.
Therefore, an evolutionary algorithm GENESIS is proposed, which resolves
uncertainty by evolving population of solutions, while considering uncertain
constraints as sub-problems, started by initial populations by a greedy
algorithm AVIDO. Theoretical analysis first justifies original problem as
NP-hard problem. Eventually, the absence of polynomial time approximation
scheme necessitates justification of original problem as multiobjective
optimization problem. Furthermore, approximation to Pareto front is verified to
be decomposed into scalar optimization sub-problems, which lays out the
theoretical foundation for decomposition based evolutionary solution. Finally,
case-study, feasibility analysis and exemplary implication are presented for
evolvable autonomic management in combined cancer treatment with in-vivo sensor
networks.
