• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.37 no.5
• /
• pp.29-44
• /
• 2000

Adaptation of operator probabilities is one of the most important and promising issues in evolutionary computation areas. This is because the setting of appropriate probabilities is not only very tedious and difficult but very important to the performance improvement of genetic algorithms. Many researchers have introduced their algorithms for setting or adapting operator probabilities. Experimental results in most previous works, however, have not been satisfiable. Moreover, Tuson have insisted that “the adaptation is not necessarily a good thing” in his papers［$^1$$^2$］. In this paper, we propose a self-tuning scheme for adapting operator probabilities in genetic algorithms. Our scheme was extensively tested on four function optimization problems and one combinational problem； and compared to simple genetic algorithms with constant probabilities and adaptive genetic algorithm proposed by Srinivas et al［$^3$］. Experimental results showed that our scheme was superior to the others. Our scheme compared with previous works has three advantages： less computational efforts, co-evolution without additional operations for evolution of probabilities, and no need of additional parameters.