Interactive Genetic Algorithm Containing Crossover and Mutation Operation
Guo Guang-son · Jisuanji gongcheng · 2015
The traditional interactive Genetic Algorithm(GA)can make user fatigue on optimizing the implicit performance index which affects the optimization quality and optimization efficiency. It is necessary to enhance the performance of interactive GA in order to apply it to complicated optimization problems successfully. The uncertainty of individual fitness is calculated based on the evaluation difference between the adjacent individuals;the convergence rate is abstracted according to the biggest information differences in evaluation sequence which reflecting the convergence of evolutionary population. Based on these,the probabilities of crossover and mutation operation of evolutionary individuals are presented. It makes the results more objective by guiding the evolutionary strategy through user preference information,and it allows a better exploration of the searching space and gives better findings compared with the traditional interactive GA(T-IGA).