The Ecology Of Computation

Propelled by advances in software design and increasing connectivity, distributed computational systems are acquiring characteristics reminiscent of social and biological organizations. This volume is a collection of articles dealing with the nature, design and implementation of these open computational systems. Although varied in their approach and methodology, the articles are related by the goal of understanding and building computational ecologies. They are grouped in three major sections. The first deals with general issues underlying open systems, studies of computational ecologies, and their similarities with social organizations. The second part deals with actual implementations of distributed computation, and the third discusses the overriding problem of designing suitable languages for open systems. All the articles are highly interdisciplinary, emphasizing the application of ecological ideas, game theory, market mechanisms, and evolutionary biology in the study of open systems.

Due to the complexity and non-linearity of most ecological problems, artificial neural networks (ANNs) have attracted attention from ecologists and environmental scientists in recent years. As these networks are increasingly being used in ecology for modeling, simulation, function approximation, prediction, classification and data mining, this unique and self-contained book will be the first comprehensive treatment of this subject, by providing readers with overall and in-depth knowledge on algorithms, programs, and applications of ANNs in ecology. Moreover, a new area of ecology, i.e., computational ecology, is proposed and its scopes and objectives are defined and discussed.Computational Ecology consists of two parts: the first describes the methods and algorithms of ANNs, interpretability and mathematical generalization of neural networks, Matlab neural network toolkit, etc., while the second provides case studies of applications of ANNs in ecology, Matlab codes, and comparisons of ANNs with conventional methods. This publication will be a valuable reference for research scientists, university teachers, graduate students and high-level undergraduates in the areas of ecology, environmental sciences, and computational science.

Graphs, networks and agent-based modeling are the most thriving and attracting sciences used in ecology and environmental sciences. As such, this book is the first comprehensive treatment of the subject in the areas of ecology and environmental sciences. From this integrated and self-contained book, researchers, university teachers and students will be provided with an in-depth and complete insight on knowledge, methodology and recent advances of graphs, networks and agent-based-modeling in ecology and environmental sciences. Java codes and a standalone software package will be presented in the book for easy use for those not familiar with mathematical details.

Ecological Informatics promotes interdisciplinary research between ecology and computer science on elucidation of principles of information processing in ecosystems, ecological sustainability by informed decision making, and bio-inspired computation. The 2nd edition of the book consolidates the scope, concepts, and techniques of this newly emerging discipline by a new preface and additional chapters on cellular automata, qualitative reasoning, hybrid evolutionary algorithms and artificial neural networks. It illustrates numerous applications of Ecological Informatics for aquatic and terrestrial ecosystems, image recognition at micro- and macro-scale as well as computer hardware design. Case studies focus on applications of artificial neural networks, evolutionary computation, cellular automata, adaptive agents, fuzzy logic as well as qualitative reasoning. The 2nd edition of the book includes an index with novel evolutionary algorithms for the discovery of multiple nonlinear functions and rule sets as well as parameter optimisation in complex ecological data.

A broad and growing literature describes the deep and multidisciplinary nature of the sustainability challenges faced by the United States and the world. Despite the profound technical challenges involved, sustainability is not, at its root, a technical problem, nor will merely technical solutions be sufficient. Instead, deep economic, political, and cultural adjustments will ultimately be required, along with a major, long-term commitment in each sphere to deploy the requisite technical solutions at scale. Nevertheless, technological advances and enablers have a clear role in supporting such change, and information technology (IT) is a natural bridge between technical and social solutions because it can offer improved communication and transparency for fostering the necessary economic, political, and cultural adjustments. Moreover, IT is at the heart of nearly every large-scale socioeconomic system-including systems for finance, manufacturing, and the generation and distribution of energy-and so sustainability-focused changes in those systems are inextricably linked with advances in IT. The focus of Computing Research for Sustainability is "greening through IT," the application of computing to promote sustainability broadly. The aim of this report is twofold: to shine a spotlight on areas where IT innovation and computer science (CS) research can help, and to urge the computing research community to bring its approaches and methodologies to bear on these pressing global challenges. Computing Research for Sustainability focuses on addressing medium- and long-term challenges in a way that would have significant, measurable impact. The findings and recommended principles of the Committee on Computing Research for Environmental and Societal Sustainability concern four areas: (1) the relevance of IT and CS to sustainability; (2) the value of the CS approach to problem solving, particularly as it pertains to sustainability challenges; (3) key CS research areas; and (4) strategy and pragmatic approaches for CS research on sustainability.

Due to the complexity and non-linearity of most ecological problems, artificial neural networks (ANNs) have attracted attention from ecologists and environmental scientists. This book provides readers with knowledge on algorithms, programs, and applications of ANNs in ecology. It proposes computational ecology.

Ecological community data. Spatial pattern analysis. Species-abundance relations. Species affinity. Community classification. Community ordination. Community interpretation.

This book describes the models, methods and algorithms that are most useful for analysing the ever-increasing supply of molecular sequence data, with a view to furthering our understanding of the evolution of genes and genomes.