The loss of life and devastation in the Gulf coast region of the United States after the hurricane season of 2005 has led to considerable debate about how to recover from the damage and mitigate damage from future incidents. This document reports the experiences of four major floods since 1948 (two in the United States, one in the Netherlands, and one in China), to draw lessons for the Gulf coast restoration effort.
This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate and postgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation.
This book systematically narrates the fundamentals, methods, and recent advances of evolutionary deep neural architecture search chapter by chapter. This will provide the target readers with sufficient details learning from scratch. In particular, the method parts are devoted to the architecture search of unsupervised and supervised deep neural networks. The people, who would like to use deep neural networks but have no/limited expertise in manually designing the optimal deep architectures, will be the main audience. This may include the researchers who focus on developing novel evolutionary deep architecture search methods for general tasks, the students who would like to study the knowledge related to evolutionary deep neural architecture search and perform related research in the future, and the practitioners from the fields of computer vision, natural language processing, and others where the deep neural networks have been successfully and largely used in their respective fields.
Defrosting for Air Source Heat Pumps: Research, Analysis and Methods presents a detailed analysis of the methods, processes and problems relating to defrosting, a necessary requirement to maintain the performance of ASHP units. Readers will gain a deeper understanding of control strategies and system design optimization methods that improve the performance and reliability of units. The book discusses the most recent experimental and numerical studies of reverse cycle defrosting and the most widely used defrosting method for ASHP. Techno-economic considerations are also presented, as is the outlook for the future. This book is a valuable resource for research students and academics of thermal energy and mechanical engineering, especially those focusing on defrosting for ASHP, heating, ventilation and energy efficiency, as well as engineers and professionals engaged in the development and management of heat pump machinery. Includes MATLAB codes that allow the reader to implement the knowledge they have acquired in their own simulations and projects Discusses experimental and numerical studies to provide a well-rounded analysis of technologies, methods and available systems Presents techno-economic considerations and a look to the future
This book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The book is divided into six parts. In Part I, it provides an introduction to production scheduling, existing solution methods, and the GP approach to production scheduling. Characteristics of production environments, problem formulations, an abstract GP framework for production scheduling, and evaluation criteria are also presented. Part II shows various ways that GP can be employed to solve static production scheduling problems and their connections with conventional operation research methods. In turn, Part III shows how to design GP algorithms for dynamic production scheduling problems and describes advanced techniques for enhancing GP’s performance, including feature selection, surrogate modeling, and specialized genetic operators. In Part IV, the book addresses how to use heuristics to deal with multiple, potentially conflicting objectives in production scheduling problems, and presents an advanced multi-objective approach with cooperative coevolution techniques or multi-tree representations. Part V demonstrates how to use multitask learning techniques in the hyper-heuristics space for production scheduling. It also shows how surrogate techniques and assisted task selection strategies can benefit multitask learning with GP for learning heuristics in the context of production scheduling. Part VI rounds out the text with an outlook on the future. Given its scope, the book benefits scientists, engineers, researchers, practitioners, postgraduates, and undergraduates in the areas of machine learning, artificial intelligence, evolutionary computation, operations research, and industrial engineering.
The loss of life and devastation in the Gulf coast region of the United States after the hurricane season of 2005 has led to considerable debate about how to recover from the damage and mitigate damage from future incidents. This document reports the experiences of four major floods since 1948 (two in the United States, one in the Netherlands, and one in China), to draw lessons for the Gulf coast restoration effort.
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