Automated design of search algorithms based on reinforcement learning

Automated algorithm design has attracted increasing research attention recently in the evolution-ary computation community. The main design decisions include selection heuristics and evolution operators in the search algorithms. Most existing studies, however, have focused on the automated design of evolution operators, neglecting selection heuristics for evolution and for replacement, not to mention considering all of the design decisions. This limited the scope of the algorithms under consideration. This study aims to systematically investigate automated design of search algorithms by exploring the impact of individual algorithmic components within a general search framework and the synergy among these multiple algorithmic components utilising a reinforce-ment learning technique. Comprehensive computational experiments are conducted on different benchmark instances of the capacitated vehicle routing problem with time windows to evaluate the effectiveness and generality of the proposed method. This study contributes to knowledge discovery in automated algorithm design using machine learning towards significantly enhanced generality of search algorithms.