Intelligent Decision Making Through Bio Inspired Optimization
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Intelligent Decision Making Through Bio-Inspired Optimization
Academic scholars, entrenched in the complexities of various domains, face the daunting task of navigating intricate decision-making scenarios. The prevailing need for efficient and effective decision-making tools becomes increasingly apparent as traditional methodologies struggle to keep pace with the demands of modern research and industry. This pivotal issue necessitates a shift, urging scholars to explore unconventional approaches that can transcend disciplinary boundaries and unlock new dimensions of problem-solving. In response to these pressing challenges, Intelligent Decision Making Through Bio-Inspired Optimization emerges as a beacon of ingenuity. This groundbreaking book transcends usual disciplinary boundaries, seamlessly integrating computer science, artificial intelligence, optimization, and decision science. Its multidisciplinary approach addresses the inherent complexities faced by scholars, offering a comprehensive exploration of nature-inspired algorithms such as genetic algorithms, swarm intelligence, and evolutionary strategies. The book's core mission is to empower academic scholars with the tools to overcome contemporary decision-making hurdles, providing a holistic understanding of these bio-inspired approaches and their potential to revolutionize the scholarly landscape.
Bio-Inspired Intelligence for Smart Decision-Making
In today's complex and fast-paced world, decision-making is critical to problem-solving across industries and academia. However, traditional optimization techniques often need help to cope with the challenges posed by dynamic and intricate environments. This limitation hampers decision-makers' ability to tackle complex problems and seize opportunities effectively. As such, there is a pressing need for innovative approaches that can enhance decision-making processes, enabling individuals and organizations to navigate uncertainty and achieve optimal outcomes. Bio-Inspired Intelligence for Smart Decision-Making offers a compelling solution to this challenge. By exploring the intersection of bio-inspired optimization techniques and decision-making, this book presents a fresh perspective that can revolutionize decisions. The book introduces readers to powerful bio-inspired algorithms such as genetic algorithms, swarm intelligence, and evolutionary strategies through a multidisciplinary lens that encompasses computer science, artificial intelligence, optimization, and decision science. These algorithms mimic natural systems' efficiency and adaptability, offering a robust framework for researchers, graduate students, and professionals who are addressing complex decision-making problems in diverse fields.
Fuzzy Intelligent Systems
Author: E. Chandrasekaran
language: en
Publisher: John Wiley & Sons
Release Date: 2021-09-08
FUZZY INTELLIGENT SYSTEMS A comprehensive guide to Expert Systems and Fuzzy Logic that is the backbone of artificial intelligence. The objective in writing the book is to foster advancements in the field and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and those in education and research covering a broad cross section of technical disciplines. Fuzzy Intelligent Systems: Methodologies, Techniques, and Applications comprises state-of-the-art chapters detailing how expert systems are built and how the fuzzy logic resembling human reasoning, powers them. Engineers, both current and future, need systematic training in the analytic theory and rigorous design of fuzzy control systems to keep up with and advance the rapidly evolving field of applied control technologies. As a consequence, expert systems with fuzzy logic capabilities make for a more versatile and innovative handling of problems. This book showcases the combination of fuzzy logic and neural networks known as a neuro-fuzzy system, which results in a hybrid intelligent system by combining a human-like reasoning style of neural networks. Audience Researchers and students in computer science, Internet of Things, artificial intelligence, machine learning, big data analytics and information and communication technology-related fields. Students will gain a thorough understanding of fuzzy control systems theory by mastering its contents.