Utilising restricted for-loops in genetic programming

Genetic programming is an approach that utilises the power of evolution to allow computers to evolve programs. While loops are natural components of most programming languages and appear in every reasonably-sized application, they are rarely used in genetic programming. The work is to investigate a number of restricted looping constructs to determine whether any significant benefits can be obtained in genetic programming. Possible benefits include: Solving problems which cannot be solved without loops, evolving smaller sized solutions which can be more easily understood by human programmers and solving existing problems quicker by using fewer evaluations. In this thesis, a number of explicit restricted loop formats were formulated and tested on the Santa Fe ant problem, a modified ant problem, a sorting problem, a visit-every-square problem and a difficult object classificat ion problem. The experimental results showed that these explicit loops can be successfully used in genetic programming. The evolutionary process can decide when, where and how to use them. Runs with these loops tended to generate smaller sized solutions in fewer evaluations. Solutions with loops were found to some problems that could not be solved without loops. The results and analysis of this thesis have established that there are significant benefits in using loops in genetic programming. Restricted loops can avoid the difficulties of evolving consistent programs and the infinite iterations problem. Researchers and other users of genetic programming should not be afraid of loops

An analysis of explicit loops in genetic programming

In this paper we analyse the reasons why evolving programs with a restricted form of loops is superior to evolving programs without loops for two problems which have underlying repetitive characteristics - a visit-every-square problem and a modified Santa Fe ant problem. We show that in the case of loops there is a larger number of solutions with smaller tree sizes. We show that the computational patterns captured in the bodies of the loops are reflective of repeating patterns in the domain. We show that the increased computational cost of evaluating an individual can be controlled by domain knowledge

An analysis of explicit loops in genetic programming

In this paper we analyse the reasons why evolving programs with a restricted form of loops is superior to evolving programs without loops for two problems which have underlying repetitive characteristics- a visitevery-square problem and a modified Santa Fe ant problem. We show that in the case of loops there is a larger number of solutions with smaller tree sizes. We show that the computational patterns captured in the bodies of the loops are reflective of repeating patterns in the domain. We show that the increased computational cost of evaluating an individual can be controlled by domain knowledge.