Rodica Ioana Lung

TL;DR: Numerical experiments indicate CESO to be an efficient method for the selected test problems compared with other evolutionary approaches.

TL;DR: A hybrid approach called Evolutionary Swarm Cooperative Algorithm based on the collaboration between a particle swarm optimization algorithm and an evolutionary algorithm is presented to deal with moving optima of optimization problems in dynamic environments.

TL;DR: This work proposes a novel approach based on game theory elements and extremal optimization to address dynamic communities detection, formulated as a mathematical game in which nodes take the role of players that seek to choose a community that maximizes their profit viewed as a fitness function.

TL;DR: A collaborative mechanism between the two methods is proposed by which the diversity provided by the multimodal technique is transmitted to the particle swarm in order to prevent its premature convergence.

TL;DR: This chapter considers the feature selection task from the point of view of classification tasks related to functional magnetic resonance imaging (fMRI) data and presents an empirical comparison of conventional LASSO-based feature selection and a novel feature selection approach designed for fMRI data based on a simple genetic algorithm.