This dissertation considers the process of code generation in a compiler: the task of transforming a program stored in some form of high level representation into a series of instructions in a low level language suitable for execution by a machine such as a computer. The problem is interpreted as one of computer program induction and optimisation.
The evolutionary computation method Linear Genetic Programming (LGP) is adapted for this task by use of a novel fitness function based upon methods a human programmer may consider to be good practice.
Two methods are considered, that of direct application of LGP and a second method which attempts to accelerate the process by subdividing the input program into smaller sections. The methods are compared and contrasted by considering the required computational effort to produce a solution to a series of sample programs, and the distribution of program lengths that result.
The dissertation concludes that LGP alone is not currently a suitable method for the task of code generation, but may act as a useful optimisation tool within some larger system.
© Mathew Carr 2010 email@example.com