HUANG YongQing1,2, HAO GuoSheng1 , LIANG ChangYong2, YANG ShanLin2
1. School of Computer Science and Technology, Xuzhou Normal University, Xuzhou 221011 2. Institute of Computer Network Systems, Hefei University of Technology, Hefei 230009
Abstract A interactive multiagent genetic algorithm (IMAGA) is proposed. Every agent fixed on a latticepoint in IMAGA interoperates with their neighbors, and the optimal one carries out selflearning to increase the energy. Hence the abilities of global convergence and local search of the algorithm are improved. In every generation, users only need to select the interested individuals instead of evaluating every individual, which simplifies the users' evaluation. The simulations of function optimization and fashion design shows that the proposed algorithm with higher convergence velocity reduces the total times of users' evaluation so as to alleviate user fatigue.
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2007, Vol. 20 
