Yu, Gwoing Tina; (1999) An Analysis of the Impact of Functional Programming Techniques on Genetic Programming. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Genetic Programming (GP) automatically generates computer programs to solve specified problems. It develops programs through the process of a "create-test-modify" cycle which is similar to the way a human writes programs. There are various functional programming techniques that human programmers can use to accelerate the program development process. This research investigated the applicability of some of the functional techniques to GP and analyzed their impact on GP performance. Among many important functional techniques, three were chosen to be included in this research, due to their relevance to GP. They are polymorphism, implicit recursion and higher-order functions. To demonstrate their applicability, a GP system was developed with those techniques incorporated. Furthermore, a number of experiments were conducted using the system. The results were then compared to those generated by other GP systems which do not support these functional features. Finally, the program search space of the general even- parity problem was analyzed to explain how these techniques impact GP performance. The experimental results showed that the investigated functional techniques have made GP more powerful in the following ways: 1) polymorphism has enabled GP to solve problems that are very difficult for standard GP to solve, i.e. nth and map programs; 2) higher-order functions and implicit recursion have enhanced GP's ability in solving the general even- parity problem to a greater degree than with any other known methods. Moreover, the analysis showed that these techniques directed GP to generate program solutions in a way that has never been previously reported. Finally, we provide the guidelines for the application of these techniques to other problems.

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