Partial Reinitialisation for Optimisers
This addresses the issue of inefficient optimization for researchers and practitioners using heuristic methods, though it is incremental as it builds on standard restart techniques.
The paper tackles the problem of heuristic optimizers getting stuck in local optima by proposing partial reinitialization, where only subsets of variables are reinitialized instead of full restarts, leading to significant improvements in solution quality within a given time for various machine learning optimization problems.
Heuristic optimisers which search for an optimal configuration of variables relative to an objective function often get stuck in local optima where the algorithm is unable to find further improvement. The standard approach to circumvent this problem involves periodically restarting the algorithm from random initial configurations when no further improvement can be found. We propose a method of partial reinitialization, whereby, in an attempt to find a better solution, only sub-sets of variables are re-initialised rather than the whole configuration. Much of the information gained from previous runs is hence retained. This leads to significant improvements in the quality of the solution found in a given time for a variety of optimisation problems in machine learning.