JACIII Vol.14 No.6 pp. 618-623
doi: 10.20965/jaciii.2010.p0618


Basic Study on Assembling of Objective Functions in Many-Objective Optimization Problems

Shun Otake, Tomohiro Yoshikawa, and Takeshi Furuhashi

Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan

November 26, 2009
May 25, 2010
September 20, 2010
nurse scheduling problem, many-objective optimization problem, assembling of objective functions, visualization
Genetic Algorithms (GAs) have been widely applied to Multiobjective Optimization Problems (MOPs), called MOGA. A set of Pareto solutions in MOPs having plural fitness functions are searched, then GA is applied in a multipoint search. MOGA performance decreases with the increasing number of objective functions because solution space spreads exponentially. An effective MOGA search is an important issue in many objective optimization problems. One effective approach is assembling objective functions and reducing their number, but appropriate assembly and the number of objective functions to be assembled has not been studied sufficiently. Our purpose here is to determine the effects of assembling objective functions by studying assembly effects when MOGA is applied to a simplified Nurse Scheduling Problem (sNSP) in two types of assembly based on objective function meaning and correlation coefficients.
Cite this article as:
S. Otake, T. Yoshikawa, and T. Furuhashi, “Basic Study on Assembling of Objective Functions in Many-Objective Optimization Problems,” J. Adv. Comput. Intell. Intell. Inform., Vol.14 No.6, pp. 618-623, 2010.
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