A Study of Genetic Algorithms Using Improvement of Objective Values as Fitness Functions

• Main Authors: Chiang Po Jen, 姜博仁
• Other Authors: Hwang Shu Yuen
• Format: Others
• Language: en_US
• Published: 1995
• Online Access: http://ndltd.ncl.edu.tw/handle/94685343455397201549

碩士 === 國立交通大學 === 資訊工程研究所 === 83 === Genetic algorithms are adaptive search techniques that are inspired from natural selection, i.e. survival of the fittest. A genetic algorithm determines how fit an individual is by its evaluation value of the objective function, i.e. a fitness function of objective values. With reproduction through generation after generation, the adaptiveness is achieved by means of the change in the proportions of individuals in a population. But when a population is subjected to long convergence in a mature run, the population will consist of similarly good individuals. Then the adaptiveness of a genetic algorithm will be lost due to the problem of declining selective pressure. We proposed a translation invariant and nonmonotonic fitness function to try to solve the problem of declining selective pressure. This new fitness function uses improvement of objective values in replace of using objective values themselves as in traditional genetic algorithms. The improvement is obtained by computing the difference of an individual's new objective value and its old objective value. Because every offspring is produced by two parents, we use the average of the two parents' objective values as an individual's old objective value. Then we translate the improvement by adding the negative value of the minimum improvement to guanrantee the positiveness of the fitness function. The empirical results showed that it succeeded in fighting the problem of declining selective pressure. In addition, the nonmonotonically way in which the new fitness function attacks an objective function provides different clues to complement the traditional genetic algorithms.