Music-Inspired Harmony Search Algorithm
Geem, Zong Woo
Sold by Kennys Bookstore, Olney, MD, U.S.A.
AbeBooks Seller since 9 October 2009
New - Hardcover
Condition: New
Quantity: 15 available
Add to basketSold by Kennys Bookstore, Olney, MD, U.S.A.
AbeBooks Seller since 9 October 2009
Condition: New
Quantity: 15 available
Add to basketThere exists an analogy between music and optimization. This book focuses on a music-inspired metaheuristic algorithm, harmony search. It details both theoretical backgrounds and practical applications of harmony search algorithms. Series: Studies in Computational Intelligence. Num Pages: 206 pages, 40 black & white tables, biography. BIC Classification: UGC; UYQ. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 14. Weight in Grams: 482. . 2009. Hardback. . . . . Books ship from the US and Ireland.
Seller Inventory # V9783642001840
Calculus has been used in solving many scientific and engineering problems. For optimization problems, however, the differential calculus technique sometimes has a drawback when the objective function is step-wise, discontinuous, or multi-modal, or when decision variables are discrete rather than continuous. Thus, researchers have recently turned their interests into metaheuristic algorithms that have been inspired by natural phenomena such as evolution, animal behavior, or metallic annealing.
This book especially focuses on a music-inspired metaheuristic algorithm, harmony search. Interestingly, there exists an analogy between music and optimization: each musical instrument corresponds to each decision variable; musical note corresponds to variable value; and harmony corresponds to solution vector. Just like musicians in Jazz improvisation play notes randomly or based on experiences in order to find fantastic harmony, variables in the harmony search algorithm have random values or previously-memorized good values in order to find optimal solution.
Calculus has been used in solving many scientific and engineering problems. For optimization problems, however, the differential calculus technique sometimes has a drawback when the objective function is step-wise, discontinuous, or multi-modal, or when decision variables are discrete rather than continuous. Thus, researchers have recently turned their interests into metaheuristic algorithms that have been inspired by natural phenomena such as evolution, animal behavior, or metallic annealing.
This book especially focuses on a music-inspired metaheuristic algorithm, harmony search. Interestingly, there exists an analogy between music and optimization: each musical instrument corresponds to each decision variable; musical note corresponds to variable value; and harmony corresponds to solution vector. Just like musicians in Jazz improvisation play notes randomly or based on experiences in order to find fantastic harmony, variables in the harmony search algorithm have random values or previously-memorized good values in order to find optimal solution.
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