In recent years, there has been a growing research interest in integrating machine learning
techniques into meta-heuristics for solving combinatorial optimization problems. This …

Evolutionary algorithms (EAs) possess strong problem-solving abilities and have been
applied in a wide range of applications. However, they still suffer from a high computational …

In recent years, the research community has witnessed an explosion of literature dealing
with the mimicking of behavioral patterns and social phenomena observed in nature towards …

Parameter tuning, that is, to find appropriate parameter settings (or configurations) of
algorithms so that their performance is optimized, is an important task in the development …

Differential evolution (DE) is one of the most popular and efficient evolutionary algorithms for
numerical optimization and it has gained much success in a series of academic benchmark …

In population-based optimization algorithms (POAs), given an optimization problem, the
quality of the solutions depends heavily on the selection of algorithms, strategies and …

Evolution has provided a source of inspiration for algorithm designers since the birth of
computers. The resulting field, evolutionary computation, has been successful in solving …

State-of-the-art multiobjective evolutionary algorithms (MOEAs) treat all the decision
variables as a whole to optimize performance. Inspired by the cooperative coevolution and …

Modern optimization algorithms typically require the setting of a large number of parameters
to optimize their performance. The immediate goal of automatic algorithm configuration is to …

Seeking multiple optima simultaneously, which multimodal optimization aims at, has
attracted increasing attention but remains challenging. Taking advantage of ant colony …