Abstract :
Unlike other genetic methods which are devoted to optimize the input data, this paper proposes an approach, CPE, aiming at finding the computation process of any problem by only using a few input and output data, consisting of the cases needed to be satisfied and those needed to be avoided. It first encodes the antibody using the method similar to that of gene expression programming (GEP), a new efficient technique of genetic programming (GP) with linear representation. Through the gradual evolution, the affinity between antibody and the non-selves become more and more intense. At the same time, every time after the chromosomes are mutated, the chromosomes should be checked to determine whether the antibody chromosome would match the selves, which are the conditions that should be satisfied. Two kind of experiment are examined in order to test the performance of the approach. The results show that CPE evolves out the data-processing processes which are exactly the same as those from which the experimental input data were generated, and compared with GP and GEP which is currently one of the most efficient genetic methods, CPE experiences shorter evolution process. Most importantly, unlike previous evolutionary methods that only consider increasing fitness, this approach takes into account both the goal (fitness) and the constraints of actual problems, which makes it possible to solve complex real problems using evolutionary computation
