Edited by professionals with years of experience, this book provides an introduction to the theory of evolutionary algorithms and single- and multi-objective optimization, and then goes on to discuss to explore applications of evolutionary algorithms for many uses with real-world applications. Covering both the theory and applications of evolutionary computation, the book offers exhaustive coverage of several topics on nontraditional evolutionary techniques, details working principles of new and popular evolutionary algorithms, and discusses case studies on both scientific and real-world applications of optimization
Sprache
Verlagsort
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Academic and Postgraduate
Produkt-Hinweis
Fadenheftung
Gewebe-Einband
Illustrationen
172 s/w Abbildungen, 34 farbige Abbildungen
34 Illustrations, color; 172 Illustrations, black and white
Maße
Höhe: 234 mm
Breite: 157 mm
Dicke: 38 mm
Gewicht
ISBN-13
978-1-77188-336-8 (9781771883368)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Ashish M. Gujarathi, PhD, is currently an Assistant Professor in the Petroleum and Chemical Engineering Department of the College of Engineering at Sultan Qaboos University, Sultanate of Oman. He was formerly a Lecturer of Chemical Engineering at the Birla Institute of Technology and Science (BITS) in Pilani, India. Dr. Gujarathi has over eleven years of experience as a chemical engineer with diverse work experience comprising a blend of academic research, teaching, and industrial consultancy work. A prolific author with articles, book chapters, and conference proceedings to his credit, he is also an editorial board member of several journals, including the Journal of Developmental Biology and Tissue Engineering and the International Open Access Journal of Biology and Computer Science and has acted as a reviewer for several international journals as well as for books and conference proceedings. His research interests include reaction engineering; process design and synthesis; process modeling, simulation, and optimization; polymer science and engineering; petrochemicals; parametric estimation and optimization of major chemical processes; evolutionary computation; and biochemical engineering.
B. V. Babu, PhD, is currently Vice Chancellor at Galgotias University in Greater Noida, India. An acknowledged researcher and renowned academician, Dr. Babu has 30 years of teaching, research, consultancy, and administrative experience. Formerly, he was the Pro Vice Chancellor of DIT University, Dehradun and founding Director of the Institute of Engineering and Technology (IET) at JK Lakshmipat University, Jaipur. He is a member of many national and international academic and administrative committees and professional organizations. Professor Babu is a distinguished academician and an acknowledged researcher and is well-known internationally for his algorithm MODE (Multi Objective Differential Evolution) and its improved variants. Overall he has over 235 research publications to his credit. He has published several books and has written chapters, invited editorials, and articles in various books, lecture notes, and international journals. He organized several international and national conferences, workshops, and seminars and also chaired several technical sessions. He has been invited speaker and has delivered keynote addresses at various international conferences, seminars, and workshops. He is the recipient of CSIR's National Technology Day Award for recognition his research work as well as of many other awards. He is the life member/fellow of many professional bodies such as IIChE, IE (I), ISTE, ICCE, IEA, SOM, ISSMO, IIIS, IAENG, SPE, ISTD, etc. He is editor-in-chief and editorial board member of several international and national scientific journals.
Theory and Applications in Engineering Systems. Introduction. Bio-Mimetic Adaptations of Genetic Algorithm and Their Applications to Chemical Engineering. Surrogate-Assisted Evolutionary Computing Methods. Evolutionary Algorithms in Ironmaking Applications. Harmony Search Optimization for Multilevel Thresholding in Digital Images. Swarm Intelligence in Software Engineering Design Problems. Gene Expression Programming in Nanotechnology Applications. Theory and Applications of Single Objective Optimization Studies. An Alternate Hybrid Evolutionary Method for Solving MINLP Problems. Differential Evolution for Optimal Design of Shell-and-Tube Heat Exchangers. Evolutionary Computation Based QoS-Aware Multicast Routing. Performance Assessment of the Canonical Genetic. An Efficient Approach for Populating Deep Web Repositories Using SFLA. Closed Loop Simulation of Quadraple Tank Process Using Adaptive Multi-Loop Fractional. Theory and Applications of Single and Multi-Objective Optimization Studies. A Practical Approach towards Multi Objective Shape Optimization. Nature-Inspired Computing Techniques for Integer Factorization. Genetic Algorithm Based Real-Time Parameter Identifier for an Adaptive Power System Stabilizer. Applied Evolutionary Computation in Fire Safety Upgrading. Elitist Multi-Objective Evolutionary Algorithms for Voltage and Reactive Power Optimization in Power Systems. Evaluation of Simulated Annealing, Differential Evolution and Particle Swarm Optimization for Solving Pooling Problems. Performance Improvement of NSGA-II Algorithm by Modifying Crossover Probability Distribution. Evolutionary Algorithms for Malware Detection and Classification. Index.
