  Search-Based Software Engineering (SBSE) is a promising paradigm that
exploits the computational search to optimize different processes when
engineering complex software systems. Self-adaptive system (SAS) is one
category of such complex systems that permits to optimize different functional
and non-functional objectives/criteria under changing environments (e.g.,
requirements and workload), which involves problems that are subject to search.
In this regard, over years, there has been a considerable amount of work that
investigates SBSE for SASs. In this paper, we provide the first systematic and
comprehensive survey exclusively on SBSE for SASs, covering papers in 27 venues
from 7 repositories, which eventually leads to several key statistics from the
most notable 74 primary studies in this particular field of research. Our
results, surprisingly, have revealed five disappointments that are of utmost
importance and can result in serve consequences but have been overwhelmingly
ignored in existing studies. We provide theoretical and/or experimental
evidence to justify our arguments against the disappointments, present
suggestions, and highlight the promising research opportunities towards their
mitigation. We also elaborate on three other emergent, but currently
under-explored opportunities for future work on SBSE for SASs. By mitigating
the disappointments revealed in this work, together with the highlighted
opportunities, we hope to be able to excite a much more significant growth in
this particular research direction.
