Chaotic exploration and learning of locomotion behaviours

We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage

Chaotic exploration and learning of locomotor behaviours

Recent developments in the embodied approach to understanding the generation of adaptive behaviour, suggests that the design of adaptive neural circuits for rhythmic motor patterns should not be done in isolation from an appreciation, and indeed exploitation, of neural-body-environment interactions. Utilising spontaneous mutual entrainment between neural systems and physical bodies provides a useful passage to the regions of phase space which are naturally structured by the neuralbody- environmental interactions. A growing body of work has provided evidence that chaotic dynamics can be useful in allowing embodied systems to spontaneously explore potentially useful motor patterns. However, up until now there has been no general integrated neural system that allows goal-directed, online, realtime exploration and capture of motor patterns without recourse to external monitoring, evaluation or training methods. For the first time, we introduce such a system in the form of a fully dynamic neural system, exploiting intrinsic chaotic dynamics, for the exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modelled as a network of neural oscillators which are coupled only through physical embodiment, and goal directed exploration of coordinated motor patterns is achieved by a chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organised dynamics each of which is a candidate for a locomotion behaviour. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states using its intrinsic chaotic dynamics as a driving force and stabilises the system on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced which results in an increased diversity of motor outputs, thus achieving multi-scale exploration. A rhythmic pattern discovered by this process is memorised and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronisation method. The dynamical nature of the weak coupling through physical embodiment allows this adaptive weight learning to be easily integrated, thus forming a continuous exploration-learning system. Our result shows that the novel neuro-robotic system is able to create and learn a number of emergent locomotion behaviours for a wide range of body configurations and physical environment, and can re-adapt after sustaining damage. The implications and analyses of these results for investigating the generality and limitations of the proposed system are discussed

Caracterización de espacios de calidad y algoritmos evolutivos en problemas de optimización con codificación real

[Resumo] Esta tese doutoral propón un procedemento de caracterización formal de algoritmos evolutivos e espazos de calidade en problemas de optimización con codificación real. A principal motivación para o desenvolvemento deste tema foi a constatación de que o gran auxe experimentado na aplicación dos algoritmos evolutivos en problemas reais cada vez máis complexos implicou o desenvolvemento de novas técnicas máis avanzadas e con mellores resultados pero, con todo, este gran nivel de actividade non veu acompañado dunha análise formal de ditas técnicas. En consecuencia, actualmente os usuarios de algoritmos evolutivos non expertos no campo posúen gran cantidade de opcións e variantes dos mesmos, pero carecen de información obxectiva sobre o ámbito de aplicación de cada un deles. Neste traballo levouse a cabo unha primeira aproximación ao desenvolvemento dun procedemento de caracterización formal destinado aos deseñadores de algoritmos evolutivos para que, á hora de presentar os seus traballos á comunidade científica, utilicen unha metodoloxía común que posibilite a caracterización práctica do algoritmo desde un punto de vista totalmente obxectivo e con conclusións que sexan facilmente utilizables por parte dos usuarios. ---- [Resmmen] Esta tesis doctoral propone un procedimiento de caracterización formal de algoritmos evolutivos y espacios de calidad en problemas de optimización con codificación real. La principal motivación para el desarrollo de este tema ha sido la constatación de que el gran auge experimentado en la aplicación de los algoritmos evolutivos en problemas reales cada vez más complejos ha implicado el desarrollo de nuevas técnicas más avanzadas y con mejores resultados pero, sin embargo, este gran nivel de actividad no ha venido acompañado de un análisis formal de dichas técnicas. En consecuencia, actualmente los usuarios de algoritmos evolutivos no expertos en el campo poseen gran cantidad de opciones y variantes de los mismos, pero carecen de información objetiva sobre el ámbito de aplicación de cada uno de ellos. En este trabajo se ha llevado a cabo una primera aproximación al desarrollo de un procedimiento de caracterización formal destinado a los diseñadores de algoritmos evolutivos para que, a la hora de presentar sus trabajos a la comunidad científica, utilicen una metodología común que posibilite la caracterización práctica del algoritmo desde un punto de vista totalmente objetivo y con conclusiones que sean fácilmente utilizables por parte de los usuarios. ---- [Abstract] This work proposes a formal characterization procedure for evolutionary algorithms and fitness landscapes in real-­‐coded optimization problems. In the last two decades, the evolutionary algorithms have been broadly applied to new and more complex problems, implying the development of new techniques that, in general, have not been analysed in formal terms. As a consequence, the final non-­‐ expert users have a wide range of algorithm options and variants, but there is a lack of objective information about their application domain. In this work it has been carried out a first approach to the development of a formal characterization procedure for evolutionary algorithm designers in order to apply a common methodology when they present their work to the scientific community, providing objective conclusions that can be easily understood by final users

Wind turbine blade geometry design based on multi-objective optimization using metaheuristics

Abstract: The application of Evolutionary Algorithms (EAs) to wind turbine blade design can be interesting, by reducing the number of aerodynamic-to-structural design loops in the conventional design process, hence reducing the design time and cost. Recent developments showed satisfactory results with this approach, mostly combining Genetic Algorithms (GAs) with the Blade Element Momentum (BEM) theory. The general objective of the present work is to define and evaluate a design methodology for the rotor blade geometry in order to maximize the energy production of wind turbines and minimize the mass of the blade itself, using for that purpose stochastic multi-objective optimization methods. Therefore, the multi-objective optimization problem and its constraints were formulated, and the vector representation of the optimization parameters was defined. An optimization benchmark problem was proposed, which represents the wind conditions and present wind turbine concepts found in Brazil. This problem was used as a test-bed for the performance comparison of several metaheuristics, and also for the validation of the defined design methodology. A variable speed pitch-controlled 2.5 MW Direct-Drive Synchronous Generator (DDSG) turbine with a rotor diameter of 120 m was chosen as concept. Five different Multi-objective Evolutionary Algorithms (MOEAs) were selected for evaluation in solving this benchmark problem: Non-dominated Sorting Genetic Algorithm version II (NSGA-II), Quantum-inspired Multi-objective Evolutionary Algorithm (QMEA), two approaches of the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), and Multi-objective Optimization Differential Evolution Algorithm (MODE). The results have shown that the two best performing techniques in this type of problem are NSGA-II and MOEA/D, one having more spread and evenly spaced solutions, and the other having a better convergence in the region of interest. QMEA was the worst MOEA in convergence and MODE the worst one in solutions distribution. But the differences in overall performance were slight, because the algorithms have alternated their positions in the evaluation rank of each metric. This was also evident by the fact that the known Pareto Front (PF) consisted of solutions from several techniques, with each dominating a different region of the objective space. Detailed analysis of the best blade design showed that the output of the design methodology is feasible in practice, given that flow conditions and operational features of the rotor were as desired, and also that the blade geometry is very smooth and easy to manufacture. Moreover, this geometry is easily exported to a Computer-Aided Design (CAD) or Computer-Aided Engineering (CAE) software. In this way, the design methodology defined by the present work was validated

Artificial Ontogenies: A Computational Model of the Control and Evolution of Development

Understanding the behaviour of biological systems is a challenging task. Gene regulation, development and evolution are each a product of nonlinear interactions between many individual agents: genes, cells or organisms. Moreover, these three processes are not isolated, but interact with one another in an important fashion. The development of an organism involves complex patterns of dynamic behaviour at the genetic level. The gene networks that produce this behaviour are subject to mutations that can alter the course of development, resulting in the production of novel morphologies. Evolution occurs when these novel morphologies are favoured by natural selection and survive to pass on their genes to future generations. Computational models can assist us to understand biological systems by providing a framework within which their behaviour can be explored. Many natural processes, including gene regulation and development, have a computational element to their control. Constructing formal models of these systems enables their behaviour to be simulated, observed and quantified on a scale not otherwise feasible. This thesis uses a computational simulation methodology to explore the relationship between development and evolution. An important question in evolutionary biology is how to explain the direction of evolution. Conventional explanations of evolutionary history have focused on the role of natural selection in orienting evolution. More recently, it has been argued that the nature of development, and the way it changes in response to mutation, may also be a significant factor. A network-lineage model of artificial ontogenies is described that incorporates a developmental mapping between the dynamics of a gene network and a cell lineage representation of a phenotype. Three series of simulation studies are reported, exploring: (a) the relationship between the structure of a gene network and its dynamic behaviour; (b) the characteristic distributions of ontogenies and phenotypes generated by the dynamics of gene networks; (c) the effect of these characteristic distributions on the evolution of ontogeny. The results of these studies indicate that the model networks are capable of generating a diverse range of stable behaviours, and possess a small yet significant sensitivity to perturbation. In the context of developmental control, the intrinsic dynamics of the model networks predispose the production of ontogenies with a modular, quasi-systematic structure. This predisposition is reflected in the structure of variation available for selection in an adaptive search process, resulting in the evolution of ontogenies biased towards simplicity. These results suggest a possible explanation for the levels of ontogenetic complexity observed in biological organisms: that they may be a product of the network architecture of developmental control. By quantifying complexity, variation and bias, the network-lineage model described in this thesis provides a computational method for investigating the effects of development on the direction of evolution. In doing so, it establishes a viable framework for simulating computational aspects of complex biological systems

Undergraduate and Graduate Course Descriptions, 2013 Summer

Wright State University undergraduate and graduate course descriptions from Summer 2013

Undergraduate and Graduate Course Descriptions, 2013 Summer

Wright State University undergraduate and graduate course descriptions from Summer 2013

Undergraduate and Graduate Course Descriptions, 2022 Summer

Wright State University undergraduate and graduate course descriptions from Summer 2022

Undergraduate and Graduate Course Descriptions, 2017 Fall

Wright State University undergraduate and graduate course descriptions from Fall 2017

Undergraduate and Graduate Course Descriptions, 2017 Fall

Wright State University undergraduate and graduate course descriptions from Fall 2017