EEG Feature Extraction Using Evolutionary Algorithms for Brain-Computer Interface Development.

Brain-computer interfaces are systems capable of mapping brain activity to specific commands, which enables to remotely automate different types of processes in hardware devices or software applications. However, the development of brain-computer interfaces has been limited by several factors that affect their performance, such as the characterization of events in brain signals and the excessive processing load generated by the high volume of data. In this paper, we propose a method based on computational intelligence techniques to handle these problems, turning them into a single optimization problem.

Implementation and integration of image processing blocks in a real-time bottle classification system.

A practical solution to the problems caused by the water, air, and soil pollution produced by the large volumes of waste is recycling. Plastic and glass bottle recycling is a practical solution but sometimes unfeasible in underdeveloped countries. In this paper, we propose a high-performance real-time hardware architecture for bottle classification, that process input image bottles to generate a bottle color as output.

2-Based Multi/Many-Objective Particle Swarm Optimization.

We propose to couple the 2 performance measure and Particle Swarm Optimization in order to handle multi/many-objective problems. Our proposal shows that through a well-designed interaction process we could maintain the metaheuristic almost inalterable and through the 2 performance measure we did not use neither an external archive nor Pareto dominance to guide the search. The proposed approach is validated using several test problems and performance measures commonly adopted in the specialized literature.

A Review of Surrogate Assisted Multiobjective Evolutionary Algorithms.

Multiobjective evolutionary algorithms have incorporated surrogate models in order to reduce the number of required evaluations to approximate the Pareto front of computationally expensive multiobjective optimization problems. Currently, few works have reviewed the state of the art in this topic. However, the existing reviews have focused on classifying the evolutionary multiobjective optimization algorithms with respect to the type of underlying surrogate model.