Combining evolutionary algorithms and agent-based simulation for the development of urbanisation policies

Urban-planning authorities continually face the problem of optimising the allocation of green space over time in developing urban environments. To help in these decision-making processes, this thesis provides an empirical study of using evolutionary approaches to solve sequential decision making problems under uncertainty in stochastic environments. To achieve this goal, this work is underpinned by developing a theoretical framework based on the economic model of Alonso and the associated methodology for modelling spatial and temporal urban growth, in order to better understand the complexity inherent in this kind of system and to generate and improve relevant knowledge for the urban planning community. The model was hybridised with cellular automata and agent-based model and extended to encompass green space planning based on urban cost and satisfaction. Monte Carlo sampling techniques and the use of the urban model as a surrogate tool were the two main elements investigated and applied to overcome the noise and uncertainty derived from dealing with future trends and expectations. Once the evolutionary algorithms were equipped with these mechanisms, the problem under consideration was defined and characterised as a type of adaptive submodular. Afterwards, the performance of a non-adaptive evolutionary approach with a random search and a very smart greedy algorithm was compared and in which way the complexity that is linked with the configuration of the problem modifies the performance of both algorithms was analysed. Later on, the application of very distinct frameworks incorporating evolutionary algorithm approaches for this problem was explored: (i) an ‘offline’ approach, in which a candidate solution encodes a complete set of decisions, which is then evaluated by full simulation, and (ii) an ‘online’ approach which involves a sequential series of optimizations, each making only a single decision, and starting its simulations from the endpoint of the previous run

Pattern-oriented calibration and validation of urban growth models: Case studies of Dublin, Milan and Warsaw

Urban growth models are established to simulate complex dynamic processes of urban development, such as urban sprawl. According to the pattern-oriented modelling (POM) paradigm, recently gaining weight in ecology as a strategy for modelling complex systems, patterns at multiple scales should be considered to reflect the underlying processes of a complex system. Yet, calibration and validation of urban growth models is typically performed with a goal function of locational (cell-by-cell) agreement only, thus not in line with POM. We therefore examined POM as an approach to calibrate and validate (constrained) cellular automata for the European cities Warsaw, Milan, and Dublin. For Milan and Warsaw, the model structures identified with POM outperformed reference solutions calibrated on a single pattern with improvements up to 25% and 30%, respectively. For Dublin, no good model structure was found, but POM did help to recognize this problem, while locational agreement only failed to do so. Furthermore, the model structures identified with POM were more diverse, i.e. including more driving factors. In these diverse structures, the importance of the neighborhood effect relative to the infrastructure and land use effects reflected the polycentricity of the city as well as its type of sprawl: from monocentric edge expansion in Dublin to in-between ribbon sprawl in Warsaw to polycentric infill development in Milan. We conclude that POM improves the robustness of urban growth model calibration and validation, and obtains more dependable information about the processes driving urban sprawl that may serve the design of instruments to limit it

Fast and Slow Changes Constrained Spatio-temporal Subpixel Mapping

Subpixel mapping (SPM) is a technique to tackle the mixed pixel problem and produce land cover and land use (LCLU) maps at a finer spatial resolution than the original coarse data. However, uncertainty exists unavoidably in SPM, which is an ill-posed downscaling problem. Spatio-temporal SPM methods have been proposed to deal with this uncertainty, but current methods fail to explore fully the information in the time-series images, especially more rapid changes over a short-time interval. In this paper, a fast and slow changes constrained spatio-temporal subpixel mapping (FSSTSPM) method is proposed to account for fast LCLU changes over a short-time interval and slow changes over a long-time interval. Namely, both fast and slow change-based temporal constraints are proposed and incorporated simultaneously into the FSSTSPM to increase the accuracy of SPM. The proposed FSSTSPM method was validated using two synthetic datasets with various proportion errors. It was also applied to oil-spill mapping using a real PlanetScope-Sentinel-2 dataset and Amazon deforestation mapping using a real Landsat-MODIS dataset. The results demonstrate the superiority of FSSTSPM. Moreover, the advantage of FSSTSPM is more obvious with an increase in proportion errors. The concepts of the fast and slow changes, together with the derived temporal constraints, provide a new insight to enhance SPM by taking fuller advantage of the temporal information in the available time-series images

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

Emergence of Convolutional Neural Network in Future Medicine: Why and How. A Review on Brain Tumor Segmentation

Manual analysis of brain tumors magnetic resonance images is usually accompanied by some problem. Several techniques have been proposed for the brain tumor segmentation. This study will be focused on searching popular databases for related studies, theoretical and practical aspects of Convolutional Neural Network surveyed in brain tumor segmentation. Based on our findings, details about related studies including the datasets used, evaluation parameters, preferred architectures and complementary steps analyzed. Deep learning as a revolutionary idea in image processing, achieved brilliant results in brain tumor segmentation too. This can be continuing until the next revolutionary idea emerging. © 2018 Behrouz Alizadeh Savareh et al., published by De Gruyter Open

Progress in the producer-scrounger game : information use and spatial models

Les animaux grégaires en quête de ressources peuvent soit consacrer leurs efforts à la recherche (stratégie producteur) ou soit attendre que les producteurs réussissent à trouver ces ressources pour les y rejoindre (stratégie chapardeur). La profitabilité de chaque option peut être analysée par le jeu producteur-chapardeur. Ce jeu a été largement exploré aux plans théorique et empirique, mais plusieurs aspects demeurent toujours inexplorés. J'ai développé cinq modèles afin d'explorer l'approvisionnement social en lien avec l'utilisation d'information et les contraintes spatiales. Le premier modèle concerne l'évolution de règles d'apprentissage, des expressions mathématiques décrivant la valeur qu'un animal accorde aux options producteur et chapardeur en fonction des gains obtenus. J'ai démontré que la règle du relative pay-off sum est évolutivement stable et donc la meilleure disponible. Les paramètres de la règle attendue demeurent intrigants et demandent maintenant à être éplorés au niveau empirique. Le second modèle explorés plutôt l'effet de l'usage d'information sociale (chapardeur) chez un prédateur en examinant son effet sur l'évolution du niveau d'agrégation de ses proies. Le modèle démontre que les proies évoluent à différents niveaux d'agrégation en réponse à l'usage d'information sociale par leurs prédateurs et que cette relation affecte à la fois l'efficacité de recherche du prédateur et la survie des proies. Le troisième modèle teste l'hypothèse, générée à partir de recherche empirique sur les oies cendrées, selon laquelle la variation du niveau de hardiesse serait associée à un dimorphisme de producteurs hardis et de chapardeurs poltrons (bold et shy, respectivement) dans le jeu producteur-chapardeur. Le modèle réfute l'existence d'un tel dimorphisme, mais démontre néanmoins un effet environnemental fort des paramètres de l'approvisionnement social sur le niveau de hardiesse d'une population. Ce résultat a d'importantes implications pour le rôle de l'utilisation d'information et les effets spatiaux dans la régulation des relations entre les producteurs et les chapardeurs. J'ai développé à partir d'une approche d'automate cellulaire un modèle producteur-chapardeur pour déterminer si une règle simple (rule of thumb) fondée sur l'apprentissage social élémentaire dans un contexte spatialement explicite pouvait prédire l'atteinte d'un équilibre producteur-chapardeur. Les résultats démontrent que l'ajout de cette règle simple génère à la fois une flexibilité comportementale significative et des dynamiques complexes qui ne sont pas habituelles à ce genre de systèmes simples. Le modèle lie l'usage d'information sociale à la structure spatiale dans un modèle déterministe. Enfin, avec le cinquième modèle j'ai exploré les effets de la géométrie du paysage (la façon dont l'espace est représenté, habituellement un quadrillage régulier) sur le jeu producteur-chapardeur. Il appert que les représentations spatiales sont un déterminant-clé dans la manière dont un jeu d'approvisionnement social d'alimentation peut réellement rendre compte de l'approvisionnement des animaux. \ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : l'approvisionnement social, effets spatiaux, l'utilisation des informations, l'apprentissage, personnalités des animau

Wireless social networks: a survey of recent advances, applications and challenges

With the ubiquitous use of smartphones and other connected pieces of equipment, the number of devices connected to the Internet is exponentially growing. This will test the efficiency of the envisioned 5G network architectures for data acquisition and its storage. It is a common observation that the communication between smart devices is typically influenced by their social relationship. This suggests that the theory of social networks can be leveraged to improve the quality of service for such communication links. In fact, the social networking concepts of centrality and community have been investigated for an efficient realization of novel wireless network architectures. This work provides a comprehensive introduction to social networks and reviews the recent literature on the application of social networks in wireless communications. The potential challenges in communication network design are also highlighted, for a successful implementation of social networking strategies. Finally, some future directions are discussed for the application of social networking strategies to emerging wireless technologies such as non-orthogonal multiple access and visible light communications

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp