Self-adaptive focusing of evolutionary effort in hierarchical genetic programming

In an attempt to address the scaling up of genetic programming to handle complex problems, we have proposed a hierarchical approach in which programs are formed from independently evolved code fragments, each of which is responsible for handling a subset of the test input cases. Although this approach offers substantial performance advantages in comparison to more conventional systems, the programs it evolves exhibit some undesirable properties for certain problem domains. We therefore propose the introduction of a self-adaptive mechanism that allows the system dynamically to focus evolutionary effort on the program components most in need. Experimentation reveals that not only does this technique lead to better-behaved programs, it also gives rise to further significant performance improvements.