Title :
Solving Expert Assignment Problem using Improved Genetic Algorithm
Author :
Li, Na-Na ; Zhang, Jian-Nan ; Gu, Jun-hua ; Liu, Bo-ying
Author_Institution :
Tianjin Univ., Tianjin
Abstract :
Expert assignment is chief and basic work of project review in project management. So it is significant to research how to solve expert assignment problem (EAP). In previous papers, we established the mathematical model of expert assignment problem, and proposed genetic algorithm (GA) and GA using heuristic mutation guide by pheromone (HMP) to solve EAP. Though it has been proven GA and HMP are effective ways for EAP, they have disadvantages of massive redundancy iteration in later period and inferior local search ability. In this paper a modification of GA which introduces adaptive mutation to HMP is proposed to solve EAP. The simulation results show that the new algorithm improves the ability of local search and generates solutions of better quality.
Keywords :
genetic algorithms; search problems; expert assignment problem; heuristic mutation; improved genetic algorithm; massive redundancy iteration; Computer science; Cybernetics; Genetic algorithms; Genetic mutations; Machine learning; Mathematical model; Optimization methods; Partitioning algorithms; Pattern recognition; Project management; Adaptive mutation; Ant algorithm; Expert assignment problem; Genetic algorithm;
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
DOI :
10.1109/ICMLC.2007.4370276