Finding appropriate parameter values for evolutionary algorithms (EA) is one of the
persisting grand challenges of the evolutionary computing (EC) field. In general, EC …

Calibrating the parameters of an evolutionary algorithm (EA) is a laborious task. The highly
stochastic nature of an EA typically leads to a high variance of the measurements. The …

This book aims to answer two questions that are fundamental to the study of agent-based
economic models: what is agent-based computational economics and why do we need …

We present and evaluate a method for estimating the relevance and calibrating the values of
parameters of an evolutionary algorithm. The method provides an information theoretic …

The sequential parameter optimization (SPOT) package for R is a toolbox for tuning and
understanding simulation and optimization algorithms. Model-based investigations are …

Evolutionary algorithms (EA) form a rich class of stochastic search methods that share the
basic principles of incrementally improving the quality of a set of candidate solutions by …

This chapter comprises the essence of several years of tutorials the authors gave on
experimental research in evolutionary computation. We highlight the renaissance of …

The ongoing changes in deregulated electricity markets creates a need of new tools for the
analysis and simulation of electricity markets. The current paper proposes a new …

Résumé Dans ce travail, nous présentons une plate-forme basée sur les algorithmes
neuroévolutionnaires qui se base sur les systèmes multiagents afin d'analyser les …

V doktorski disertaciji obravnavamo problem razporejanja proizvodnje, ki v proizvodnih
podjetjih predstavlja enega glavnih problemov, saj lahko prihaja do vsakodnevnih …