Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

On microelectronic self-learning cognitive chip systems

After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting conscious phenomena should crucially be restricted to extremely well defined constraints. Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details. In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche

Modeling and Control of a Liquid Level System Based on the Takagi-Sugeno Fuzzy Model Using the Whale Optimization Algorithm

The liquid level control remains an important task for research and is used by process control engineers. Linear models for the tank system were obtained based on empirical techniques. From these identified linear models Takagi-Sugeno model was obtained and later a fuzzy controller was designed based on the parallel distributed compensation method (PDC). A comparison was made between local linear PI controllers and the parallel distributed compensation controller. The evaluation criteria which was considered in this paper is the step response. In addition to all of the above stated, the whale optimization algorithm is implemented to fine tune parameters and to obtain optimized Takagi-Sugeno plant model. Moreover, the PDC from optimized model is compared to the first parallel distributed controller, which was based on identified models