Chang Wook Ahn – författare

This book presents selected papers from International Conference on Hybrid Intelligence: Theories and Applications (HITA 2025), held in Sri Sri University, Cuttack, Odisha, India during 20 – 21 November 2025. The topics covered in the book are fuzzy set theory, machine learning algorithms, machine learning models, deep learning, neural networks, natural language processing, swarm intelligence, metaheuristics, bio-inspired computing, and evolutionary intelligence.

This book presents selected papers from International Conference on Hybrid Intelligence: Theories and Applications (HITA 2025), held in Sri Sri University, Cuttack, Odisha, India during 20 – 21 November 2025. The topics covered in the book are fuzzy set theory, machine learning algorithms, machine learning models, deep learning, neural networks, natural language processing, swarm intelligence, metaheuristics, bio-inspired computing, and evolutionary intelligence.

Every real-world problem from economic to scientific and engineering fields is ultimately confronted with a common task, viz., optimization. Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. The goal of this book is to provide effective optimization algorithms for solving a broad class of problems quickly, accurately, and reliably by employing evolutionary mechanisms. In this regard, five significant issues have been investigated: Bridging the gap between theory and practice of GEAs, thereby providing practical design guidelines. Demonstrating the practical use of the suggested road map. Offering a useful tool to significantly enhance the exploratory power in time-constrained and memory-limited applications. Providing a class of promising procedures that are capable of scalably solving hard problems in the continuous domain. Opening an important track for multiobjective GEA research that relies on decomposition principle. This book serves to play a decisive role in bringing forth a paradigm shift in future evolutionary computation.

Every real-world problem from economic to scientific and engineering fields is ultimately confronted with a common task, viz., optimization. Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. The goal of this book is to provide effective optimization algorithms for solving a broad class of problems quickly, accurately, and reliably by employing evolutionary mechanisms. In this regard, five significant issues have been investigated: Bridging the gap between theory and practice of GEAs, thereby providing practical design guidelines. Demonstrating the practical use of the suggested road map. Offering a useful tool to significantly enhance the exploratory power in time-constrained and memory-limited applications. Providing a class of promising procedures that are capable of scalably solving hard problems in the continuous domain. Opening an important track for multiobjective GEA research that relies on decomposition principle. This book serves to play a decisive role in bringing forth a paradigm shift in future evolutionary computation.

This book focuses on soft computing and how it can be applied to solve real-world problems arising in various domains, ranging from medicine and healthcare, to supply chain management, image processing and cryptanalysis. The book is divided into two volumes and offers valuable insights into soft computing for teachers and researchers alike;

This book focuses on soft computing and how it can be applied to solve real-world problems arising in various domains, ranging from medicine and healthcare, to supply chain management, image processing and cryptanalysis. The book is divided into two volumes and offers valuable insights into soft computing for teachers and researchers alike;