Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

High performance latent dirichlet allocation for text mining

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Latent Dirichlet Allocation (LDA), a total probability generative model, is a three-tier Bayesian model. LDA computes the latent topic structure of the data and obtains the significant information of documents. However, traditional LDA has several limitations in practical applications. LDA cannot be directly used in classification because it is a non-supervised learning model. It needs to be embedded into appropriate classification algorithms. LDA is a generative model as it normally generates the latent topics in the categories where the target documents do not belong to, producing the deviation in computation and reducing the classification accuracy. The number of topics in LDA influences the learning process of model parameters greatly. Noise samples in the training data also affect the final text classification result. And, the quality of LDA based classifiers depends on the quality of the training samples to a great extent. Although parallel LDA algorithms are proposed to deal with huge amounts of data, balancing computing loads in a computer cluster poses another challenge. This thesis presents a text classification method which combines the LDA model and Support Vector Machine (SVM) classification algorithm for an improved accuracy in classification when reducing the dimension of datasets. Based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), the algorithm automatically optimizes the number of topics to be selected which reduces the number of iterations in computation. Furthermore, this thesis presents a noise data reduction scheme to process noise data. When the noise ratio is large in the training data set, the noise reduction scheme can always produce a high level of accuracy in classification. Finally, the thesis parallelizes LDA using the MapReduce model which is the de facto computing standard in supporting data intensive applications. A genetic algorithm based load balancing algorithm is designed to balance the workloads among computers in a heterogeneous MapReduce cluster where the computers have a variety of computing resources in terms of CPU speed, memory space and hard disk space

Multi-agent Collision Avoidance Using Interval Analysis and Symbolic Modelling with its Application to the Novel Polycopter

Coordination is fundamental component of autonomy when a system is defined by multiple mobile agents. For unmanned aerial systems (UAS), challenges originate from their low-level systems, such as their flight dynamics, which are often complex. The thesis begins by examining these low-level dynamics in an analysis of several well known UAS using a novel symbolic component-based framework. It is shown how this approach is used effectively to define key model and performance properties necessary of UAS trajectory control. This is demonstrated initially under the context of linear quadratic regulation (LQR) and model predictive control (MPC) of a quadcopter. The symbolic framework is later extended in the proposal of a novel UAS platform, referred to as the ``Polycopter" for its morphing nature. This dual-tilt axis system has unique authority over is thrust vector, in addition to an ability to actively augment its stability and aerodynamic characteristics. This presents several opportunities in exploitative control design. With an approach to low-level UAS modelling and control proposed, the focus of the thesis shifts to investigate the challenges associated with local trajectory generation for the purpose of multi-agent collision avoidance. This begins with a novel survey of the state-of-the-art geometric approaches with respect to performance, scalability and tolerance to uncertainty. From this survey, the interval avoidance (IA) method is proposed, to incorporate trajectory uncertainty in the geometric derivation of escape trajectories. The method is shown to be more effective in ensuring safe separation in several of the presented conditions, however performance is shown to deteriorate in denser conflicts. Finally, it is shown how by re-framing the IA problem, three dimensional (3D) collision avoidance is achieved. The novel 3D IA method is shown to out perform the original method in three conflict cases by maintaining separation under the effects of uncertainty and in scenarios with multiple obstacles. The performance, scalability and uncertainty tolerance of each presented method is then examined in a set of scenarios resembling typical coordinated UAS operations in an exhaustive Monte-Carlo analysis

Towards the "resilience thinking": the effects of forest management on ecosystem services provision

Over the last decades, human-induced effects on Earth systems increasingly undermined the availability of natural capital stocks and flows. Climate change effects, land use and cover transformations, and unsustainable management practices strongly reduced the resilience, resistance, and stability of ecosystems, through compromising biodiversity conservation and services provision. In particular, forest resources faced increasing external disturbances, stresses and impacts, thus reducing their capacity to continuously provide benefits to local communities. In order to face these substantial changes, forest management is now called to improve ecosystem resilience, mainly through implementing adaptive strategies and more sustainable practices. The aim of research is twofold: (i) to assess the effects of management strategies and practices on forest ecosystem resilience, particularly by analyzing and describing the impact of alternative management approaches on biodiversity conservation and services provision; and (ii) to provide insights on how to improve forest ecosystem resilience through implementing the “resilience thinking” in practical forest management. By providing the results from specific case studies, the research develops throughout the following steps: (i) describing how forest management approached the concepts of sustainability and resilience over the last decades, from global to local scale; (ii) reviewing the main economic and ecological foundations in assessing forest ecosystem services; (iii) giving a picture of some recent approaches for mapping and quantifying forest ecosystem services, including the use of different indicators; (iv) reporting the main effects of forest management on ecosystem services provision both at landscape and stand scale; and (v) delineating useful guidelines to implement the “resilience thinking” in forest management. Although the effects from other disturbances (i.e. climate and land use changes) are not treated here, the main research findings may give a substantial contribution to deeper understand the role of forest management in improving forest ecosystem resilience, as well as to better orient adaptive strategies from stand to landscape scale towards ensuring both forest health and vitality and benefits to local communities in the future.Negli ultimi decenni, gli effetti dell’uomo sui sistemi terrestri hanno crescentemente minacciato la disponibilità dello stock e dei flussi di capitale naturale. Gli effetti dei cambiamenti climatici, le trasformazioni nell’uso e copertura del suolo, e le pratiche di gestione non sostenibile hanno fortemente ridotto la resilienza, la resistenza e la stabilità degli ecosistemi, compromettendo così la conservazione della biodiversità e l’approvvigionamento dei servizi ecosistemici. In particolare, le risorse forestali si sono confrontate con disturbi esterni, stress e impatti, riducendo così la loro capacità di fornitura di benefici alle comunità locali. Al fine di confrontarsi con questi cambiamenti sostanziali, la gestione forestale è chiamata a migliorare la resilienza ecosistemica, prevalentemente attraverso l’adozione di strategie adattative e pratiche più sostenibili. L’obiettivo di questa ricerca è duplice: (i) valutare gli effetti delle pratiche e strategie gestionali sulla resilienza degli ecosistemi forestali, in particolare mediante l’analisi e la descrizione degli impatti di approcci alternativi di gestione sulla conservazione della biodiversità e l’approvvigionamento dei servizi; e (ii) fornire approfondimenti su come migliorare la resilienza degli ecosistemi forestali mediante l’adozione del “pensiero resiliente” nella gestione forestale pratica. Attraverso la presentazione dei risultati di casi-studio specifici, la ricerca si sviluppa sui passaggi seguenti: (i) descrivere come la gestione forestale ha approcciato i concetti di sostenibilità e resilienza negli ultimi decenni, dalla scala globale a quella locale; (ii) rivedere i principali fondamenti economici ed ecologici nella valutazione dei servizi ecosistemici forestali; (iii) fornire un’immagine di alcuni approcci recenti alla mappatura e quantificazione dei servizi ecosistemici forestali, includendo l’uso di diversi indicatori; (iv) riportare gli effetti principali della gestione forestale sull’approvvigionamento dei servizi ecosistemici sia a scala di paesaggio che di popolamento; (v) tracciare delle linee-guida utili all’adozione del “pensiero resiliente” nella gestione forestale. Sebbene gli effetti di altri disturbi (come, per esempio, i cambiamenti climatici o nell’uso del suolo) non sono trattati, i principali risultati di ricerca possono dare un contributo sostanziale a una più approfondita comprensione del ruolo della gestione forestale nel miglioramento della resilienza degli ecosistemi forestali, così come a un miglior orientamento della gestione adattativa dalla scala di popolamento a quella di paesaggio, volta ad assicurare sia la vitalità e la salute degli ecosistemi forestali, sia i benefici alle comunità locali.Dottorato di ricerca in Management and conservation issues in changing landscapes (XXVI ciclo

Interaction dynamics and autonomy in cognitive systems

The concept of autonomy is of crucial importance for understanding life and cognition. Whereas cellular and organismic autonomy is based in the self-production of the material infrastructure sustaining the existence of living beings as such, we are interested in how biological autonomy can be expanded into forms of autonomous agency, where autonomy as a form of organization is extended into the behaviour of an agent in interaction with its environment (and not its material self-production). In this thesis, we focus on the development of operational models of sensorimotor agency, exploring the construction of a domain of interactions creating a dynamical interface between agent and environment. We present two main contributions to the study of autonomous agency: First, we contribute to the development of a modelling route for testing, comparing and validating hypotheses about neurocognitive autonomy. Through the design and analysis of specific neurodynamical models embedded in robotic agents, we explore how an agent is constituted in a sensorimotor space as an autonomous entity able to adaptively sustain its own organization. Using two simulation models and different dynamical analysis and measurement of complex patterns in their behaviour, we are able to tackle some theoretical obstacles preventing the understanding of sensorimotor autonomy, and to generate new predictions about the nature of autonomous agency in the neurocognitive domain. Second, we explore the extension of sensorimotor forms of autonomy into the social realm. We analyse two cases from an experimental perspective: the constitution of a collective subject in a sensorimotor social interactive task, and the emergence of an autonomous social identity in a large-scale technologically-mediated social system. Through the analysis of coordination mechanisms and emergent complex patterns, we are able to gather experimental evidence indicating that in some cases social autonomy might emerge based on mechanisms of coordinated sensorimotor activity and interaction, constituting forms of collective autonomous agency

Characterising and modeling the co-evolution of transportation networks and territories

The identification of structuring effects of transportation infrastructure on territorial dynamics remains an open research problem. This issue is one of the aspects of approaches on complexity of territorial dynamics, within which territories and networks would be co-evolving. The aim of this thesis is to challenge this view on interactions between networks and territories, both at the conceptual and empirical level, by integrating them in simulation models of territorial systems.Comment: Doctoral dissertation (2017), Universit\'e Paris 7 Denis Diderot. Translated from French. Several papers compose this PhD thesis; overlap with: arXiv:{1605.08888, 1608.00840, 1608.05266, 1612.08504, 1706.07467, 1706.09244, 1708.06743, 1709.08684, 1712.00805, 1803.11457, 1804.09416, 1804.09430, 1805.05195, 1808.07282, 1809.00861, 1811.04270, 1812.01473, 1812.06008, 1908.02034, 2012.13367, 2102.13501, 2106.11996