This book looks at the process of human cognition and the way complex problems are solved by decomposing them into a list of strategic objectives, before focusing individually on each objective to plan for a tactical solution. This process has been formulated by military planners in the form of the Standard Operating Procedure, by which problem solving is organised into four different stages: deliberation, planning, war meeting and plan execution. This has enabled the development of a methodology for problem solving in a dynamic environment. This is illustrated with the help of a six-case study in chess and prediction of exchange rate movement in a foreign exchange market.

This book provides basic management knowledge in a clearly structured way. Fundamental aspects of management are described, on the basis of which a model of the enterprise is outlined. This allows readers to find their way around easily, to reflect, then to set new approaches in context and examine them in a critical light. The practical examples, the interpretation questions, and the short case studies at the end of the chapters facilitate the transition from theory to practice.

Based on a broad range of case studies, Organization and Management Problem Solving is an insightful text designed to improve the application of organization theory and systems thinking in teaching and practice. This book illustrates the five key themes in the nature of organization and managementa'technical, structural, psychosocial, managerial, and culturala'through the analysis of measured incidents tested by students. A clear theoretical framework supports the case studies, allowing the text to have practical relevance to contemporary settings and to be recognized as a model for describing, analyzing, and responding to organization and management problems. The model integrates the thinking of many writers on organization and problem solving including Ackoff, Blake, and Mouton; Schein, Kast, and Rosenweign; and Mitroff and Lippitt. The approach eliminates causal conditions and emphasizes responsive problem solving. Theory is applied and expanded as needed to a broader social context, engaging the reader in a thorough understanding of the nature and development of organization theory and problem solving. This book is relevant to consultants, academics, and professional managers in a number of settings (academic, military, business organizations, and research institutes) and disciplines (including development and change, management, human resources, social psychology, communication, sociology, and psychology).

This book offers a process for conceiving solutions to complex, wicked, messy, swampy or socio-technical problems. When charged with complex problem solving, a useful set of concepts needs to emerge, be agreed, and acted upon. Using relevant examples a

Dynamic environments abound, encompassing many real-world problems in fields as diverse as finance, engineering, biology and business. A vibrant research literature has emerged which takes inspiration from evolutionary processes to develop problem-solvers for these environments. 'Foundations in Grammatical Evolution for Dynamic Environments' is a cutting edge volume illustrating current state of the art in applying grammar-based evolutionary computation to solve real-world problems in dynamic environments. The book provides a clear introduction to dynamic environments and the types of change that can occur. This is followed by a detailed description of evolutionary computation, concentrating on the powerful Grammatical Evolution methodology. It continues by addressing fundamental issues facing all Evolutionary Algorithms in dynamic problems, such as how to adapt and generate constants, how to enhance evolvability and maintain diversity. Finally, the developed methods are illustrated with application to the real-world dynamic problem of trading on financial time-series. The book was written to be accessible to a wide audience and should be of interest to practitioners, academics and students, who are seeking to apply grammar-based evolutionary algorithms to solve problems in dynamic environments. 'Foundations in Grammatical Evolution for Dynamic Environments' is the second book dedicated to the topic of Grammatical Evolution.

Explores best practices in assisting students in understanding engineering concepts through interactive and virtual environments.

Evolutionary Algorithms (EAs) have grown into a mature field of research in optimization, and have proven to be effective and robust problem solvers for a broad range of static real-world optimization problems. Yet, since they are based on the principles of natural evolution, and since natural evolution is a dynamic process in a changing environment, EAs are also well suited to dynamic optimization problems. Evolutionary Optimization in Dynamic Environments is the first comprehensive work on the application of EAs to dynamic optimization problems. It provides an extensive survey on research in the area and shows how EAs can be successfully used to continuously and efficiently adapt a solution to a changing environment, find a good trade-off between solution quality and adaptation cost, find robust solutions whose quality is insensitive to changes in the environment, find flexible solutions which are not only good but that can be easily adapted when necessary. All four aspects are treated in this book, providing a holistic view on the challenges and opportunities when applying EAs to dynamic optimization problems. The comprehensive and up-to-date coverage of the subject, together with details of latest original research, makes Evolutionary Optimization in Dynamic Environments an invaluable resource for researchers and professionals who are dealing with dynamic and stochastic optimization problems, and who are interested in applying local search heuristics, such as evolutionary algorithms.

Details robustness, stability, and performance of Evolutionary Algorithms in dynamic environments

Many complex systems in civil and military operations are highly automated with the intention of supporting human performance in difficult cognitive tasks involving information processing. The complex systems can involve teams or individuals working on real-time tasks where a number of constraints must be satisfied. Decision Making in Complex Environments addresses the role of the human, the technology and the processes in complex socio-technical and technological systems.