Based On Linear Transformation Fitness Function And The Robot Evolutionary Computation

Now the computer is tending to more and more intelligent, and no matter what kind of applications, intelligence has been the most important factor. In the 1980s, the artificial intelligence theory based on the evolution of structure — computational intelligence was fast becoming the new mainstream. Computational intelligence consists of a wide range of research areas which have profound links and promote each other, and evolutionary computation is just an important field of these areas.The thinking of evolutionary robotics mainly comes from evolutionary computation. In evolutionary robotics, the primary work of designers is to decide evolutionary framework and assessment strategies. The system behaviors depend to a high degree on the fitness function used in assessing. In designing methods of fitness function there are many ways, for examples: linear transformation, power transformation, index transformation, variance adjustment, etc. Meanwhile, the parameters in evolutionary framework, such as the selection probability, crossover probability and mutation probability are crucial to the evolutionary process and results. The target of this paper is to achieve the integration of the populations' diversity and convergence, evolution speed and performance, thereby optimize the design of evolutionary framework and assessment strategies, with the design of fitness function and evolutionary parameters. This is of great significance to the development of evolutionary robotics and evolutionary computation.This paper includes the following studies: research of thechoices of fitness function and the improvement of evolutionary parameters with linear transformation, focusing on the choices of fitness function. In linear transformation, the ratio coefficient and the scope coefficient ultimately determine the outcomes, and the scope coefficient is determined by the ratio coefficient and the fitness average coefficient. Depending on different evolutionary stages, we will establish the self-adapting expressions of the ratio coefficient and the fitness average coefficient, and then obtain three kinds of evolutionary computation based on linear transformation which have different fitness functions according to ratio, scope, ratio and scope. And the construction of evolutionary parameters depends on different stages of evolution and the size of fitness.The innovations are included in this study. The self-adapting expressions of ratio coefficient, fitness average coefficient and evolutionary parameters which alternate by the early or late stage of evolution are established, and then the mathematical models for three fitness functions and evolutionary parameters based on linear transformation are proposed and the corresponding algorithms are designed. After that, the related simulation experiments are implemented on platform Evorobot, then the simulation results are analyzed and the corresponding conclusions are got.