The Effects of Transfer of Global Improvements in Genetic Programming

Koza has shown how Automatically Defined Functions (ADFs) can reduce computational effort in the genetic programming paradigm. In Koza's Automatically Defined Functions, as well as in standard genetic programming, an improvement in a part of a program (an ADF or a main body) can only be transferred to other individuals in the population via crossover. In this article, we consider whether it is a good idea to transfer immediately improvements found by a single individual to other individuals in the population. A system that implements this idea has been proposed and tested for the even-5-parity, even-6-parity, and even-10-parity problems. Results are very encouraging: computational effort is reduced (compared to Koza's ADFs) and the system seems to be less prone to early stagnation. Also, as evolution occurs in separate populations, our approach permits to parallelize genetic programming in another different way

Tag-based modules in genetic programming

In this paper we present a new technique for evolving mod-ular programs with genetic programming. The technique is based on the use of “tags ” that evolving programs may use to label and later to refer to code fragments. Tags may refer inexactly, permitting the labeling and use of code fragments to co-evolve in an incremental way. The technique can be implemented as a minor modification to an existing, general purpose genetic programming system, and it does not re-quire pre-specification of the module architecture of evolved programs. We demonstrate that tag-based modules readily evolve and that this allows problem solving effort to scale well with problem size. We also show that the tag-based module technique is effective even in complex, non-uniform problem environments for which previous techniques per-form poorly. We demonstrate the technique in the context of the stack-based genetic programming system PushGP, but we also briefly discuss ways in which it may be used with other kinds of genetic programming systems