  In the last years, multi-objective evolutionary algorithms (MOEA) have been
applied to different software engineering problems where many conflicting
objectives have to be optimized simultaneously. In theory, evolutionary
algorithms feature a nice property for runtime optimization as they can provide
a solution in any execution time. In practice, based on a Darwinian inspired
natural selection, these evolutionary algorithms produce many deadborn
solutions whose computation results in a computational resources wastage:
natural selection is naturally slow. In this paper, we reconsider this founding
analogy to accelerate convergence of MOEA, by looking at modern biology
studies: artificial selection has been used to achieve an anticipated specific
purpose instead of only relying on crossover and natural selection (i.e.,
Muller et al [18] research on artificial mutation of fruits with X-Ray).
Putting aside the analogy with natural selection , the present paper proposes
an hyper-heuristic for MOEA algorithms named Sputnik 1 that uses artificial
selective mutation to improve the convergence speed of MOEA. Sputnik leverages
the past history of mutation efficiency to select the most relevant mutations
to perform. We evaluate Sputnik on a cloud-reasoning engine, which drives
on-demand provisioning while considering conflicting performance and cost
objectives. We have conducted experiments to highlight the significant
performance improvement of Sputnik in terms of resolution time.
