Genetic programming that ensures programs are original

Conventional genetic programming (GP) does not guarantee no revisits, i.e., a program may be generated for fitness evaluations more than one time. This is clearly wasteful in applications that involve expensive and/or time consuming fitness evaluations. This paper proposes a new GP-non-revisiting genetic programming NrGP-that guarantees that all programs generated is original. The basic idea is to use memory to store all programs generated. To increase efficiency in indexing and storage, the memory is organized as an S-expression trie. Since the number of solutions generated is modest for applications involving expensive and/or time consuming fitness evaluations, the extra memory needed is manageable. GP and NrGP are compared using two GP bench mark problems, namely, the symbolic regression and the even N-parity problem. It is found that NrGP outperforms GP, significantly reducing the computational effort (CE) required. This clearly shows the power of the idea of ensuring no revisits. It is anticipated that the same non-revisiting idea can be applied to other types of GP to enhance their efficiency. A new CE measurement is also reported that removes some statistical biases associated with the conventional CE.