@article{MAKHILLJEAS2018132217185,
    title = {Finding a Good Global Sequence Using Multi-Level Genetic Algorithm},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {22},
    pages = {9777-9783},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.9777.9783},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.9777.9783},
    author = {Zeyd,Esraa and},
    keywords = {LLVM,population,sequences,experiments,-O2 flag,performance},
    abstract = {Trying all the optimization sequences manually to find out a one that give the best performance is
not practical solution. Therefore, it is essential to layout a schema which is able to introduce an optimization
sequence with better performance for a given function. In this research, multi-levels genetic algorithm has been
used to find a good optimal sequence. Our method has three levels. In the first level, the programs search space
is divided into three groups and try to find a good sequence for each program in group. These good sequences
for each program will be used as initial seed to find good sequence for all programs in that group. This process
will be repeated for all three groups to find good sequence for each one. Then, these good sequences from
three groups will be used as a seed for initial population to the third level. Genetic algorithm will use the
resulting sequences to find out one good optimal sequence for all these groups. LLVM compiler framework has
been used to validate the proposed method. Experiments that have been implemented on the generated good
sequence for different benchmarks show the effectiveness of the proposed method. Overall, it achieves better
performance compare with the -O2 flag.}
    }