Editorial: Memetic Computing: Accelerating optimization heuristics with problem-dependent local search methods

Abstract

Memetic Algorithms and, in general, approaches underneath the wider Memetic Computing paradigm, have been at the core of a frantic research activity since the very inception of this research area in the late eighties. The community working in this area has so far showcased the benefits of hybridizing population-based algorithms with trajectory-based methods or any other specialized procedures that encompass problem-specific knowledge in a variety of real-world scenarios. From the perspective of the algorithms themselves, this hybridization can be realized in many different ways: it is this upsurge of manifold algorithmic approaches what has maintained a vigorous and intense activity around Memetic Computing over the years, progressively adapting the paradigm to newly emerging problem formulations and characteristics. This editorial introduces the readership of Swarm and Evolutionary Computation to the contributions finally included in the Special Issue on Memetic Computing: Accelerating Optimization Heuristics with Problem-Dependent Local Search Methods. The high quality of the works presented in this editorial unquestionably proves the excellent health of this vibrant research area, as well as its continued success at tackling challenging real-world optimization problems.