An Evolution Strategy (ES) is one of the fundamental algorithms among Evolutionary Algorithms (EA) that utilize a population of candidate solutions and bio-inspired operators to search for a target solution. ES is primarily used for optimization of real-valued vectors. The algorithm operators are iteratively applied within a loop, where each loop run is called a generation (g), until a termination criterion is met. Variation is accomplished by the so-called mutation operator. For real-valued search spaces, mutation is normally performed by adding a normally (Gaussian) distributed random value to each component under variation (i.e., to each parameter encoded in the individuals). Algorithm 1 shows a pseudo-code description of a typical ES.
FE-SEM images of the bio-inspired structures with a leaf like morphology are shown in Fig. 1. The images clearly show that the increase of time and voltage during the reaction stimulate the structure growth. The structures grow as a result of the solid– vapor reactions between the silver substrate and the reactive sulfur atmosphere. The proposed growth mechanism for these structures is similar to the sulfidation of resistors for the electronic industry . Initially, S -2 ions react with silver (Ag) and forms silver
What is being attempted in this study is finding a solution to a very difficult real problem requiring high evaluation times and managing large vectors of numbers which encompass a huge search space, very hard to explore by exhaustive methods. Furthermore, there is no analytic equation, so traditional methods are not viable. In addition, low complexity operations as used in metaheuristics are needed. All these reasons make this problem suitable for solving with a bio-inspired algorithm . Concretely, we have designed a new evolutionary algorithm, based on a (10+2)-EA  and called Eco-friendly Route Algorithm. EfRA is an elitist steady state EA, with a population of ten individuals, generating two new individuals at each step, mainly because the evaluation of each individual requires a simulation which takes more than 30 seconds to complete. EfRA is a light-weight algorithm (compared to other metaheuristics like common EAs, PSOs, etc.), it performs well without the need for an analytic equation which is impossible in this domain.
ABSTRACT. Aircraft aerodynamic forces and moments can be expressed as a func- tion of the dynamic pressure, aircraft dimensions and ﬂight conditions. They are very important do predict the aircraft behavior and performance. Moreover, they play an important role predicting the model response to control inputs. For these reasons it is important to accurate determine the aerodynamic characteristics in terms of stability and control derivatives. This study presents a methodology to precisely estimate stability and control derivatives through system identiﬁcation procedure. Navion FAR 23 airplane model was used for this purpose. A bio-inspired optimization method was used to create optimal input control signals to excite the model and obtain an output signal with good frequency content that allowed to properly identify the system. Inclusion of bio-inspired methods increased the accu- racy of the estimates. The method can be used to identify ﬁxed wing platforms of similar characteristics. Results can be used to develop ﬂight simulators to collect system information regarding certiﬁcation evidences and to train pilots.
Recent studies have shown that the correct labeling of phonetic classes may help current Automatic Speech Recognition (ASR) when combined with classical parsing automata based on Hidden Markov Models (HMM). Through the present paper a method for Phonetic Class Labeling (PCL) based on bio-inspired speech processing is described. The methodology is based in the automatic detection of formants and formant trajectories after a careful separation of the vocal and glottal components of speech and in the operation of CF (Characteristic Frequency) neurons in the cochlear nucleus and cortical complex of the human auditory apparatus. Examples of phonetic class labeling are given and the applicability of the method to Speech Processing is discussed.
It is described the design of a mechatronic system to actuate a hand soft orthotic device for rehabilitation and assistance purposes developed by the author of this thesis within the Bio Mechatronics Department of Fraunhofer Institute for Manufacturing Engineering and Automation (IPA) based in Stuttgart, Germany. The system mimics the musculoskeletal anatomy and kinesiology of the human body by resembling the bone-muscle-tendon configuration. A key feature of the orthosis is that allows the individual movement of the fingers. The actuation consists in the use of -high contraction- Festo Pneumatic Artificial Muscles (PAMs) within a 3D printed support structure which was designed using anthropometric data to aim to comfort and ergonomics. The PAMs are operated with piezoelectric -flow proportional- valves. The sensors mimic the human somatosensory system to control the motion and to confer a haptic nature to the human interface. The use of light indicators allows visual reinforcement during exercises. The final deliverable is a testing model that is going to be used for further experiments. Finally, this orthotic device is envisioned to become a mobile solution for self- aided rehabilitation.
Evolutionary Multi-objective Optimization has been extensively studied in the last few years. However, there is a research gap to tackle multi-objective optimization in dynamic environments using nature-inspired algorithms. The main goal of this thesis was to contribute in this research area by proposing algorithms able to tackle DMOPs with competitive results. Therefore, in this thesis, the proposal of two dif- ferent DMOEAs was presented. The first DMOEA, called Immune GDE3, consists of a hybrid approach which combines the advantages of Differential Evolution and the Artificial Immune System. In Immune GDE3, the Generalized Differential Evolution (GDE3) algorithm was adopted as MOP optimizer, while an immune response based on the clonal selection principle was used as change reaction mechanism (see Chap- ter 5). On the other hand, the proposal of a new DMOEA based on performance indicator was also presented. The indicator-based DMOEA was called DIGDE. This algorithm is an improved version of Immune GDE3. The main difference with re- spect to Immune GDE3 is based on the use of IGD contributions in its selection mechanism. Therefore, different from Immune GDE3 which uses only crowding dis- tance, DIGDE uses the contributions to IGD to select the best individuals for next generations (see Chapter 6).
Genetic Algorithms (GAs) are search techniques inspired by Darwinian Evolution and developed by Holland in the 1970s . In a GA, an initial population of individu- als, i.e. possible solutions defined within the domain of a fitness function to be opti- mized, is evolved by means of genetic operators: selection, crossover and mutation. The selection operator ensures the survival of the fittest individuals, crossover repre- sents the mating between individuals, and mutation introduces random modifications into the population. GAs possess effective capabilities to explore the search space in parallel, exploiting the information about the quality of the individuals evaluated so far . Using the crossover operator, GAs combine the features of parents to pro- duce new and better solutions, which preserve the parents’ best characteristics. Using the mutation operator, new information is introduced in the population in order to explore new areas of the search space. The strategy known as elitism, which is a vari- ant of the general process of constructing a new population, allows the best organisms from the current generation to survive to the next, remaining unaltered. At the end of the process, the population of solutions is expected to converge to the global optimum of the fitness function.
Advances in nanotechnology and molecular biology have promoted material development using bio-inspired approaches . Nano-defined self-assemblies derived from biological systems have been used as an inspiration for the innovative development of materials, such as bio-adhesives that could work efficiently in water using cross-linked non-toxic components. Some animals and plants produce adhesive secretions for prey capture, defence, prevention of dehydration, and camouflage, among other things; and have been used as the inspiration for the design of new adhesives to be applied in the medical, bio-electronical, textile and cosmetics industries [ 2, 3]. Recent examples include mussels, frogs, ivy plants, sandcastle worms, geckos, sea cucumbers and tubeworms [4 – 12]. Each organism has its own features and the physicochemical characterization of biological derivate secretions is challenging. In general, these secretions are composed mainly of mixtures of proteins, carbohydrates, surfactants, peptides, water and some ions like Ca 2+ . Natural adhesives usually consist of complex biopolymer blends, forming in many cases extracellular nanometric structures that play a key role in the adhesion mechanism. Some of the functions of the extracellular nanostructures are attributed to the enhancement of energy dissipation, as it is frequently found in climbing animals that produce fibrillary structures. These structures are thought to be responsible for a mechanism analogous to the molecular stretching of polymeric chains and also, through their nanostructures, to influence the contact points with the target surfaces to minimize crack length and propagation .
Cognitive Speech Perception is a field of growing interest as far as studies in cognitive sciences have advanced during the last decades helping in providing better descriptions on neural processes taking place in sound processing by the Auditory System and the Auditory Cortex. This knowledge may be applied to design new bio-inspired paradigms in the processing of speech sounds in Speech Sciences, especially in Articulatory Phonetics, but in many others as well, as Emotion Detection, Speaker’s Characterization, etc. The present paper reviews some basic facts already established in Speech Perception and the corresponding paradigms under which these may be used in designing new algorithms to detect Articulatory (Phonetic) Features in speech sounds which may be later used in Speech Labelling, Phonetic Characterization or other similar tasks.
. A robotic platform called BATMAV (fully actuated by SMA wires) is described in both papers. Thereby, SMAs have been used for two purposes: first, as muscle-like actuators that provide the flapping and morphing wingbeat motions of the bat robot, and second, as super-elastic flexible hinges that join the wing’s bone structure. Most of the experiments in  were carried out with a two degree of freedom wing capable of flapping at 3 Hz. Despite the fact that their robot is able to achieve accurate bio-inspired trajectories, the results presented lack experimental evidence of aerodynamics measurements that might demonstrate the viability of their proposed design. Moreover, neither  nor  detail how to control the SMAs to achieve the bio-inspired motion of BATMAV’s wings.
Cabe resaltar que el local se encuentra muy cercano al centro comercial Mega Plaza y Plaza Norte. Asimismo, se observa los distritos colindantes como los Olivos y S.M.P. Siendo estos distritos de lima norte con un alto comercio y transito urbano, lo cual permite que nuestro mercado objetivo de un NSE B y C, tenga una red de distribución para nuestro Bio cubierto de gran alcance en los consumidores.