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 …

Parameter control is aimed at realizing performance gains through a dynamic choice of the
parameters which determine the behavior of the underlying optimization algorithm. In the …

Abstract The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-based
EA framework that has been shown to perform well in several domains, including Genetic …

The growth and development of scientific applications have demanded the creation of
efficient resource management systems. Resource provisioning and scheduling are two …

Abstract The (1+(λ, λ))(1+(λ, λ)) genetic algorithm proposed in Doerr et al.(Theor Comput Sci
567: 87–104, 2015) is one of the few examples for which a super-constant speed-up of the …

Most of the real-world black-box optimization problems are associated with multiple non-
linear as well as non-convex constraints, making them difficult to solve. In this work, we …

The heavy-tailed mutation operator proposed in Doerr et al.(GECCO 2017), called fast
mutation to agree with the previously used language, so far was successfully used only in …

Adapting applications to optimally utilize available hardware is no mean feat: the plethora of
choices for optimization techniques are infeasible to maximize manually. To this end, auto …

This paper proposes a new evolutionary algorithm, called DSMGA-II, to efficiently solve
optimization problems via exploiting problem substructures. The proposed algorithm adopts …

The (1+(λ, λ)) genetic algorithm, first proposed at GECCO 2013, showed a surprisingly good
performance on some optimization problems. The theoretical analysis so far was restricted …