Résumé:
Choosing the most suitable algorithm for tackling black-box continuous
optimization problem (BBOP) is a difficult task. As numerous optimization
algorithms are evaluated annually, there is an urgent need for automated
methods for algorithm selection for single-objective black-box optimization. This
technique is better known as the Algorithm Selection Problem, where an
effective algorithm tailored to each particular problem can be chosen for that
particular problem. It gained much importance in the last decades to find a
means by which researchers could identify some existing algorithms best suited
for working on particular problems, rather than inventing new ones. We will be
proposing, in this paper, a dynamic adaptive model that shall make use of Deep
Learning (DL) technology coupled with Exploratory Landscape Analysis
techniques (ELA) for predicting the single best solver for any given problem set.
In particular, we combine the Exploratory landscape analysis techniques to
visualize the landscape of problem features with a number of high-performance
algorithm portfolios to create deep learning models which predict the optimal
solver for any given problem set. It outperforms the performance of
conventional optimization algorithms on continuous black-box optimization
problems. The proposed DL model will adapt dynamically to changing
characteristics, concerning problem instances, thus being even more effective
and efficient.
