Interactive multi-agent genetic algorithm

A interactive multi-agent genetic algorithm (IMAGA) is proposed. Every agent fixed on a lattice-point in IMAGA interoperates with their neighbors, and the optimal one carries out self-learning 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 show that the proposed algorithm with higher convergence velocity reduces the total times of users' evaluation so as to alleviate user fatigue.