Genetic Programming Intron's Improvement And Its Application For One-Machine Scheduling Problems

Genetic programming (GP) is a new technology for optimization, which simulate inherit and evolution in the nature and get optimal solutions through reproduction, crossover and mutation operations.In this paper, firstly, the basic algorithms and theory are described, then the research state and advances in home and abroad are systematic summarized. Some new trends of development of genetic programming are indicated. Then, intron and convergence of genetic programming are studied by means of symbolic regression and formula discovery in genetic programming. False convergence is analyzed, and for its reason and the way to minimizing its effects are proposed. Intron's affections on convergence of genetic programming are studied. By testing a great deal of examples, the affections of reproduction and crossover on intron are discussed. Then, an improved crossover called single-parent crossover is recommended, and two of its employed means are recommended.Production scheduling problems have become a major application field for evolutionary computation methods. In this paper the potential usage of genetic programming for the solution of the one-machine total tardiness problem is studied. At present there are two research methods, firstly, a combination dispatching rule is utilized as an indirect way of representing a permutation through a genetic programming framework; secondly, a traditional genetic programming framework are employed as a basis for evolving a formula of a dispatching rule for the one-machine total tardiness problem. Although genetic programming for production scheduling problems is very elementary and need to be further studied, but the research have been extensively enlarged the field of application ofgenetic programming.