Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation


Book Description

Evolutionary computation has emerged as a major topic in the scientific community as many of its techniques have successfully been applied to solve problems in a wide variety of fields. Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation provides comprehensive research on emerging theories and its aspects on intelligent computation. Particularly focusing on breaking trends in evolutionary computing, algorithms, and programming, this publication serves to support professionals, government employees, policy and decision makers, as well as students in this scientific field.




International Journal of Applied Evolutionary Computation, Issue 3


Book Description

The International Journal of Applied Evolutionary Computation (IJAEC) covers state-of-the-art interdisciplinary research on emerging areas and of intelligent computation (IC). By providing an academic and scientific forum for exchanging high quality results on innovative topics, trends and research in the field of IC, this journal expands the fields and the depths of its most principal and critical concepts. IJAEC extends existing research findings (theoretical innovations and modeling applications) to provide the highest quality original concepts, hybrid applications, innovative methodologies, and the development trends studies for all audiences. IJAEC publishes three categories of papers: research papers, research notes, and research review. Research papers have significant original research findings. The research must be complete and contribute substantially to knowledge in the field. Research notes comprise research that is complete but not as comprehensive as to meet the criteria of a full research paper. Research reviews are novel, insightful, and carefully crafted articles that conceptualize research areas and synthesize prior research. Research review articles must provide new insights that advance our understanding of the research areas and help in identifying and developing future research directions.







Evolutionary Computation for Modeling and Optimization


Book Description

Concentrates on developing intuition about evolutionary computation and problem solving skills and tool sets. Lots of applications and test problems, including a biotechnology chapter.
















Evolutionary Computation with Intelligent Systems


Book Description

This book focuses on cutting-edge innovations and core theories, principles, and algorithms applicable to a wide area. Real-life applications, case studies, and examples are included along with emerging trends, design, and optimized solutions pivoting around the needs of Society 5.0. Evolutionary Computation with Intelligent Systems: A Multidisciplinary Approach to Society 5.0 provides a holistic view of evolutionary computation techniques including principles, procedures, and future applications with real-life examples. The book comprehensively explains evolutionary computation, design, principles, development trends, and optimization and describes how it can transform the operating context of the organization. It exemplifies the potential of evolutionary computation for the next generation and the role of cloud computing in shaping Society 5.0. It also provides insight into various platforms, paradigms, techniques, and tools used in diverse fields. This book appeals to a variety of readers such as academicians, researchers, research scholars, and postgraduates.




Evolutionary Computation: Theory And Applications


Book Description

Evolutionary computation is the study of computational systems which use ideas and get inspiration from natural evolution and adaptation. This book is devoted to the theory and application of evolutionary computation. It is a self-contained volume which covers both introductory material and selected advanced topics. The book can roughly be divided into two major parts: the introductory one and the one on selected advanced topics. Each part consists of several chapters which present an in-depth discussion of selected topics. A strong connection is established between evolutionary algorithms and traditional search algorithms. This connection enables us to incorporate ideas in more established fields into evolutionary algorithms. The book is aimed at a wide range of readers. It does not require previous exposure to the field since introductory material is included. It will be of interest to anyone who is interested in adaptive optimization and learning. People in computer science, artificial intelligence, operations research, and various engineering fields will find it particularly interesting.