The improvement of genetic algorithm using Boltzmann selection

유전자 알고리즘에서 볼쯔만 선택방법의 개선

In this paper, we propose a method to improve Genetic Algorithm using Boltzmann selection which Michael has suggested. But Michael uses temperature schedule(the initial temperature, the cooling rate), which can be applicable only to the limited range of problems. We propose a new method to find the critical temperature and the cooling rate as parameters of the temperature schedule. The critical temperature can be derived from the distribution of each individual＇s fitness. Through the application of the island model where each island has differing cooling rate, it is proved that it is unnecessary to find the optimal cooling rate. The simulation on the TSP＇s with various city sizes proves the proposed critical temperature correct.