TY  - BOOK
AU  - Geem,Zong Woo
ED  - SpringerLink (Online service)
TI  - Music-Inspired Harmony Search Algorithm: Theory and Applications 
T2  - Studies in Computational Intelligence,
SN  - 9783642001857
AV  - TA329-348 
U1  - 519 23
PY  - 2009///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Engineering
KW  - Artificial intelligence
KW  - Acoustics
KW  - Engineering mathematics
KW  - Music
KW  - Appl.Mathematics/Computational Methods of Engineering
KW  - Artificial Intelligence (incl. Robotics)
N1  - Harmony Search as a Metaheuristic Algorithm -- Overview of Applications and Developments in the Harmony Search Algorithm -- Harmony Search Methods for Multi-modal and Constrained Optimization -- Solving NP-Complete Problems by Harmony Search -- Harmony Search Applications in Mechanical, Chemical and Electrical Engineering -- Optimum Design of Steel Skeleton Structures -- Harmony Search Algorithms for Water and Environmental Systems -- Identification of Groundwater Parameter Structure Using Harmony Search Algorithm -- Modified Harmony Methods for Slope Stability Problems -- Harmony Search Algorithm for Transport Energy Demand Modeling -- Sound Classification in Hearing Aids by the Harmony Search Algorithm -- Harmony Search in Therapeutic Medical Physics
N2  - 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
UR  - http://dx.doi.org/10.1007/978-3-642-00185-7
ER  -