Most machine learning algorithms are configured by a set of hyperparameters whose values
must be carefully chosen and which often considerably impact performance. To avoid a time …

Abstract Machine learning algorithms have been used widely in various applications and
areas. To fit a machine learning model into different problems, its hyper-parameters must be …

This paper presents the results and insights from the black-box optimization (BBO)
challenge at NeurIPS2020 which ran from July–October, 2020. The challenge emphasized …

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 …

Real-world optimization problems have been comparatively difficult to solve due to the
complex nature of the objective function with a substantial number of constraints. To deal …

This work proposes two novel optimization algorithms called Salp Swarm Algorithm (SSA)
and Multi-objective Salp Swarm Algorithm (MSSA) for solving optimization problems with …

It has long been observed that for practically any computational problem that has been
intensely studied, different instances are best solved using different algorithms. This is …

Benchmarking is a cornerstone in the analysis and development of computational methods,
especially in the field of evolutionary computation, where theoretical analysis of the …

Environmental models are used extensively to evaluate the effectiveness of a range of
design, planning, operational, management and policy options. However, the number of …

Bayesian Optimization (BO), the application of Bayesian function approximation to finding
optima of expensive functions, has exploded in popularity in recent years. In particular, much …