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 …

More than a decade after the first extensive overview on parameter control, we revisit the
field and present a survey of the state-of-the-art. We briefly summarize the development of …

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

Program autotuning has been shown to achieve better or more portable performance in a
number of domains. However, autotuners themselves are rarely portable between projects …

This survey compiles ideas and recommendations from more than a dozen researchers with
different backgrounds and from different institutes around the world. Promoting best practice …

Adaptive operator selection (AOS) is used to determine the application rates of different
operators in an online manner based on their recent performances within an optimization …

A variety of Reinforcement Learning (RL) techniques blends with one or more techniques
from Evolutionary Computation (EC) resulting in hybrid methods classified according to their …

Adopting FPGA as an accelerator in datacenters is becoming mainstream for customized
computing, but the fact that FPGAs are hard to program creates a steep learning curve for …

Evolutionary algorithms (EAs) are robust stochastic optimisers that perform well over a wide
range of problems. Their robustness, however, may be affected by several adjustable …

Abstract The Dynamic Vehicle Routing Problem (DVRP) is a complex variation of classical
Vehicle Routing Problem (VRP). The aim of DVRP is to find a set of routes to serve multiple …