A Comparative Study of Genetic Programming and Grammatical Evolution for Evolving Data Structures

The research presented in the paper forms part of a larger initiative aimed at automatic algorithm induction using machine learning. This paper compares the performance of two machine learning techniques, namely, genetic programming and a variation of genetic programming, grammatical evolution, for automatic algorithm induction. The application domain used to evaluate both the approaches is the induction of data structure algorithms. Genetic programming is an evolutionary algorithm that searches a program space for an algorithm/program which when executed will provide a solution to the problem at hand. Grammatical evolution is a variation of genetic programming which provides a more flexible encoding, thereby eliminating the sufficiency and closure requirement imposed by genetic programming. The paper firstly extends previous work on genetic programming for evolving data structures, providing an alternative genetic programming solution to the problem. A grammatical evolution solution to the problem is then presented. This is the first application of grammatical evolution to this domain and for the simultaneous induction of algorithms. The performance of these approaches in inducing algorithms for the stack and queue data structures are compared. Keywords—algorithm induction; genetic programming; grammatical evolution; automatic programming