TY - CHAP
T1 - Genetic Algorithms for Organizational Design and Inspired by Organizational Theory
AU - Yu, Tian Li
AU - Yassine, Ali A.
AU - Goldberg, David E.
N1 - Publisher Copyright:
© 2007 by Idea Group Inc. All rights reserved.
PY - 2006/1/1
Y1 - 2006/1/1
N2 - Modularity is widely used in system analysis and design such as complex engineering products and their organization, and modularity is also the key to solving optimization problems efficiently via problem decomposition. We first discover modularity in a system and then leverage this knowledge to improve the performance of the system. In this chapter, we tackle both problems with the alliance of organizational theory and evolutionary computation. First, we cluster the dependency structure matrix (DSM) of a system using a simple genetic algorithm (GA) and an information theoretic-based metric. Then we design a better GA through the decomposition of the optimization problem using the proposed DSM clustering method.
AB - Modularity is widely used in system analysis and design such as complex engineering products and their organization, and modularity is also the key to solving optimization problems efficiently via problem decomposition. We first discover modularity in a system and then leverage this knowledge to improve the performance of the system. In this chapter, we tackle both problems with the alliance of organizational theory and evolutionary computation. First, we cluster the dependency structure matrix (DSM) of a system using a simple genetic algorithm (GA) and an information theoretic-based metric. Then we design a better GA through the decomposition of the optimization problem using the proposed DSM clustering method.
UR - https://www.scopus.com/pages/publications/105014435296
UR - https://www.scopus.com/pages/publications/105014435296#tab=citedBy
U2 - 10.4018/978-1-59140-984-7.ch028
DO - 10.4018/978-1-59140-984-7.ch028
M3 - Chapter
AN - SCOPUS:105014435296
SN - 9781591409847
VL - II
SP - 412
EP - 428
BT - Handbook of Research on Nature-Inspired Computing for Economics and Management
ER -