Diversification-Based Learning in Computing and Optimization

TL;DR

Diversification-Based Learning (DBL) is a broad, flexible framework that extends opposition-based learning, enhancing metaheuristic search strategies and applicable to various machine learning and optimization problems.

Contribution

The paper unifies and extends existing metaheuristic principles into a comprehensive DBL framework that surpasses opposition-based learning in flexibility and scope.

Findings

DBL offers more flexible strategies for intensification and diversification.

DBL extends previous metaheuristic approaches with broader applicability.

Potential applications span multiple subfields of machine learning and optimization.

Abstract

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.