One Of The Keystones In Practical Metaheuristic Problem-solving Is The Fact That Tuning The Optimization Technique To The Problem Under Consideration Is Crucial For Achieving Top Performance. This Tuning/customization Is Usually In The Hands Of The Algorithm Designer, And Despite Some Methodological Attempts, It Largely Remains A Scientific Art. Transferring A Part Of This Customization Effort To The Algorithm Itself -endowing It With Smart Mechanisms To Self-adapt To The Problem- Has Been A Long Pursued Goal In The Field Of Metaheuristics. These Mechanisms Can Involve Different Aspects Of The Algorithm, Such As For Example, Self-adjusting The Parameters, Self-adapting The Functioning Of Internal Components, Evolving Search Strategies, Etc. Recently, The Idea Of Hyperheuristics, I.e., Using A Metaheuristic Layer For Adapting The Search By Selectively Using Different Low-level Heuristics, Has Also Been Gaining Popularity. This Volume Presents Recent Advances In The Area Of Adaptativeness In Metaheuristic Optimization, Including Up-to-date Reviews Of Hyperheuristics And Self-adaptation In Evolutionary Algorithms, As Well As Cutting Edge Works On Adaptive, Self-adaptive And Multilevel Metaheuristics, With Application To Both Combinatorial And Continuous Optimization.