Following decades of sustained improvement, metaheuristics are one of the great success
stories of optimization research. However, in order for research in metaheuristics to avoid …

Abstract Machine learning and optimisation are two growing fields of artificial intelligence
with an enormous number of computer science applications. The techniques in the former …

Over the last three decades, a large number of evolutionary algorithms have been
developed for solving multi-objective optimization problems. However, there lacks an upto …

This paper describes jMetalPy, an object-oriented Python-based framework for multi-
objective optimization with metaheuristic techniques. Building upon our experiences with the …

Feature selection is a necessary critical stage in data mining process. There is always an
arm race to build frameworks and libraries that ease and automate this process. In this …

In this work we present an overview of the most prominent population-based algorithms and
the methodologies used to extend them to multiple objective problems. Although not exact in …

In presence of multiple objectives to be optimized in Search-Based Software Engineering
(SBSE), Pareto search has been commonly adopted. It searches for a good approximation of …

Migrating existing enterprise software to cloud platforms involves the comparison of
competing cloud deployment options (CDOs). A CDO comprises a combination of a specific …

New contributions in the field of iterative optimisation heuristics are often made in an
iterative manner. Novel algorithmic ideas are not proposed in isolation, but usually as …

This article introduces SESSL (S imulation E xperiment S pecification via a S cala L ayer), an
embedded domain-specific language for simulation experiments. It serves as an additional …