Evolutionary Algorithms and Agricultural Systems deals with the practical application of evolutionary algorithms to the study and management of agricultural systems. The rationale of systems research methodology is introduced, and examples listed of real-world applications. It is the integration of these agricultural systems models with optimization techniques, primarily genetic algorithms, which forms the focus of this book. The advantages are outlined, with examples of agricultural models ranging from national and industry-wide studies down to the within-farm scale. The potential problems of this approach are also discussed, along with practical methods of resolving these problems. Agricultural applications using alternate optimization techniques (gradient and direct-search methods, simulated annealing and quenching, and the tabu search strategy) are also listed and discussed. The particular problems and methodologies of these algorithms, including advantageous features that may benefit a hybrid approach or be usefully incorporated into evolutionary algorithms, are outlined. From consideration of this and the published examples, it is concluded that evolutionary algorithms are the superior method for the practical optimization of models of agricultural and natural systems. General recommendations on robust options and parameter settings for evolutionary algorithms are given for use in future studies. Evolutionary Algorithms and Agricultural Systems will prove useful to practitioners and researchers applying these methods to the optimization of agricultural or natural systems, and would also be suited as a text for systems management, applied modeling, or operations research.

Evolutionary Algorithms and Agricultural Systems deals with the practical application of evolutionary algorithms to the study and management of agricultural systems. The rationale of systems research methodology is introduced, and examples listed of real-world applications. It is the integration of these agricultural systems models with optimization techniques, primarily genetic algorithms, which forms the focus of this book. The advantages are outlined, with examples of agricultural models ranging from national and industry-wide studies down to the within-farm scale. The potential problems of this approach are also discussed, along with practical methods of resolving these problems. Agricultural applications using alternate optimization techniques (gradient and direct-search methods, simulated annealing and quenching, and the tabu search strategy) are also listed and discussed. The particular problems and methodologies of these algorithms, including advantageous features that may benefit a hybrid approach or be usefully incorporated into evolutionary algorithms, are outlined. From consideration of this and the published examples, it is concluded that evolutionary algorithms are the superior method for the practical optimization of models of agricultural and natural systems. General recommendations on robust options and parameter settings for evolutionary algorithms are given for use in future studies. Evolutionary Algorithms and Agricultural Systems will prove useful to practitioners and researchers applying these methods to the optimization of agricultural or natural systems, and would also be suited as a text for systems management, applied modeling, or operations research.

Darwinian evolutionary theory is one of the most important theories in human history for it has equipped us with a valuable tool to understand the amazing world around us. There can be little surprise, therefore, that Evolutionary Computation (EC), inspired by natural evolution, has been so successful in providing high quality solutions in a large number of domains. EC includes a number of techniques, such as Genetic Algorithms, Genetic Programming, Evolution Strategy and Evolutionary Programming, which have been used in a diverse range of highly successful applications. This book brings together some of these EC applications in fields including electronics, telecommunications, health, bioinformatics, supply chain and other engineering domains, to give the audience, including both EC researchers and practitioners, a glimpse of this exciting rapidly evolving field.

Evolutionary algorithms are bio-inspired algorithms based on Darwin’s theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods. In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms. Chapter 1 describes a generic evolutionary algorithm as well as the basic operators that compose it. Chapter 2 is devoted to the solving of continuous optimization problems, without constraint. Three leading approaches are described and compared on a set of test functions. Chapter 3 considers continuous optimization problems with constraints. Various approaches suitable for evolutionary methods are presented. Chapter 4 is related to combinatorial optimization. It provides a catalog of variation operators to deal with order-based problems. Chapter 5 introduces the basic notions required to understand the issue of multi-objective optimization and a variety of approaches for its application. Finally, Chapter 6 describes different approaches of genetic programming able to evolve computer programs in the context of machine learning.

Agri 4.0 and the Future of Cyber-Physical Agricultural Systems is the first book to explore the potential use of technology in agriculture with the focus on the technologies, enabling the reader to better comprehend the full range of CPS opportunities. From planning to distribution, CPS technologies are available to impact agricultural output, delivery and consumption. The impact for food security may be significant and this book explores ways to implement CPS effectively and appropriately. Technology, especially computing technology, can play a significant in the field of agriculture by processing digitized data to solve the complex agronomic, agricultural demand and supply issues that impact the food supply chain, and ultimately food security. In Agri 4.0, the cyber physical system synchronously interacts with agricultural systems to control and execute the operation autonomously. Digitalization of agriculture integrates digital computers to assist the processes of agriculture with its digitized data and its allied technology including AI, Computer Vision, Big data, Block chain and IoT. Agri 4.0 digitalizes, estimate, plan, predict, and produce the optimum agricultural inputs and outputs for the required for commercial purposes. It can be used to get a fair, transparent and accountable process to serve the stakeholders. The convergence of IoT, ML, Big data and 5G networks have opened new possibilities to explore and exploit the cyber physical agricultural systems. The management and practices of smart multi-layer architecture and smart supply chain are one of the key application areas in Agri 4.0. The global team of authors also presents important insights into promising areas of precision agriculture, autonomous systems, smart farming environment, smart production monitoring, pest detection and recovery, sustainable industrial practices and government policies in Agri 4.0. - Addresses one of the most complex applications of CPS - Describes various technologies, covering CPS in agriculture from precision agriculture to smart supply chain management - Focuses on the digital framework, tools, and systems capable of supporting Agri 4.0

This book comprises a selection of papers from the EVOLVE 2012 held in Mexico City, Mexico. The aim of the EVOLVE is to build a bridge between probability, set oriented numerics and evolutionary computing, as to identify new common and challenging research aspects. The conference is also intended to foster a growing interest for robust and efficient methods with a sound theoretical background. EVOLVE is intended to unify theory-inspired methods and cutting-edge techniques ensuring performance guarantee factors. By gathering researchers with different backgrounds, a unified view and vocabulary can emerge where the theoretical advancements may echo in different domains. Summarizing, the EVOLVE focuses on challenging aspects arising at the passage from theory to new paradigms and aims to provide a unified view while raising questions related to reliability, performance guarantees and modeling. The papers of the EVOLVE 2012 make a contribution to this goal.

Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally hard optimization problems. With the widespread use of intelligent systems in recent years, evolutionary algorithms have been applied, beyond classical optimization problems, to AI system parameter optimization and the design of artificial neural networks and feature selection in machine learning systems. This volume will present recent results of applications of the most successful metaheuristics, from differential evolution and particle swarm optimization to artificial neural networks, loT allocation, and multi-objective optimization problems. It will also provide a broad view of the role and the potential of evolutionary algorithms as service components in Al systems.

This book arises from a 2011 international conference entitled Climate Change, Agri-Food, Fisheries and Ecosystems: Reinventing Research, Innovation, and Policy Agendas for an Environmentally and Socially-Balanced Growth (ICCAFFE2011), organized by the North-South Center for Social Sciences (NRCS) in collaboration with the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH, Germany and the Institute for Research and Development (IRD), France. Coverage focuses on the agricultural sector and helps improve understanding of the relationships between agriculture and the environment and between human communities and nature, so as to sustainably manage agricultural development. The contributors analyze the interdependence between sustainable agricultural development and environmental, economic and social dynamics; assess the impacts of soil degradation on agricultural productivity; present ways to enhance livestock production and recommend mechanisms for managing links between agro-biodiversity, climate change and livelihoods. Part I examines sustainable agriculture development and environmental, economic and social dynamics, addressing topics such as global climate change, agriculture and challenges including socio-economic factors, adaptation, poverty reduction and water management. Part II covers the impacts of soil degradation on agricultural productivity and discusses the use of expert systems to assess and manage degraded lands, agricultural productivity, land suitability and rehabilitation. Part III focuses on livestock production enhancements, such as feed resources and supplemental feeds for animals and capacity building for women in dairy management. Part IV shares the outcomes of research in agro-biodiversity, climate change and livelihoods, addressing topics such as co-management of forests, carbon consumption models, biodiversity conservation and carbon sequestration and scarce mangrove forests. Current environmental and agro-ecological research focuses on understanding the cause-consequence relationships between specific agricultural practices and land use and the responses at different levels of ecosystems. This book is intended to contribute to this discussion.