Transferring part of this customization effort to the algorithm itself-menganugrahkan so with a clever mechanism to self-adapt to the problem has been a goal long pursued in the field of metaheuristics. This mechanism may involve different aspects of the algorithm, such as for example, adjusting the parameters, self-adapting function of the internal components, developing search strategy, etc. Recently, the idea hyperheuristics, namely, using layers to adapt the metaheuristic search with the selective use of different low-level heuristics, also have gained popularity. This volume presents the latest advances in the field of adaptativeness in metaheuristic optimization, including studies of up-to-date hyperheuristics and self-adaptation in evolutionary algorithms, and cutting edge work on metaheuristics adaptive, self-adaptive and stratified, with applications in both combinatorial and continuous optimization .