  Diversification-Based Learning (DBL) derives from a collection of principles
and methods introduced in the field of metaheuristics that have broad
applications in computing and optimization. We show that the DBL framework goes
significantly beyond that of the more recent Opposition-based learning (OBL)
framework introduced in Tizhoosh (2005), which has become the focus of numerous
research initiatives in machine learning and metaheuristic optimization. We
unify and extend earlier proposals in metaheuristic search (Glover, 1997,
Glover and Laguna, 1997) to give a collection of approaches that are more
flexible and comprehensive than OBL for creating intensification and
diversification strategies in metaheuristic search. We also describe potential
applications of DBL to various subfields of machine learning and optimization.
