This book argues for the importance of sketching as a mode of thinking, and the relevance of sketching in the design process, design education, and design practice. Through a wide range of analysis and discussion, the book looks at the history of sketching as a resource throughout the design process and asks questions such as: where does sketching come from? When did sketching become something different to drawing and how did that happen? What does sketching look like in the present day? Alongside an in-depth case study of students, teachers, and practitioners, this book includes a fascinating range of interviews with designers from a wide variety of backgrounds, including fashion, user experience, and architecture. Sketching as Design Thinking explains how drawing and sketching remain a prominent aspect in our learning and creative process, and provides a rich resource for students of visual art and design.
This book argues for the importance of sketching as a mode of thinking, and the relevance of sketching in the design process, design education, and design practice. Through a wide range of analysis and discussion, the book looks at the history of sketching as a resource throughout the design process and asks questions such as: where does sketching come from? When did sketching become something different to drawing and how did that happen? What does sketching look like in the present day? Alongside an in-depth case study of students, teachers, and practitioners, this book includes a fascinating range of interviews with designers from a wide variety of backgrounds, including fashion, user experience, and architecture. Sketching as Design Thinking explains how drawing and sketching remain a prominent aspect in our learning and creative process, and provides a rich resource for students of visual art and design.
Metaheuristic algorithms are considered as generic optimization tools that can solve very complex problems characterized by having very large search spaces. Metaheuristic methods reduce the effective size of the search space through the use of effective search strategies. Book Features: Provides a unified view of the most popular metaheuristic methods currently in use Includes the necessary concepts to enable readers to implement and modify already known metaheuristic methods to solve problems Covers design aspects and implementation in MATLAB® Contains numerous examples of problems and solutions that demonstrate the power of these methods of optimization The material has been written from a teaching perspective and, for this reason, this book is primarily intended for undergraduate and postgraduate students of artificial intelligence, metaheuristic methods, and/or evolutionary computation. The objective is to bridge the gap between metaheuristic techniques and complex optimization problems that profit from the convenient properties of metaheuristic approaches. Therefore, engineer practitioners who are not familiar with metaheuristic computation will appreciate that the techniques discussed are beyond simple theoretical tools, since they have been adapted to solve significant problems that commonly arise in such areas.
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