Evolving conditional branch programs in Tierra-based Asynchronous Genetic Programming

This paper explores the methods which can evolve conditional branch programs in Tierra-based Asynchronous Genetic Programming (TAGP) to improve an evolutionary ability for complex programs. For this purpose, we propose three methods, namely, the label address, the elite preserving strategy with the program size restriction, and the gradient fitness calculation. An intensive experiment on a calculation program evolution reveals the following implications: (1) the label addressing can simply construct the conditional branch; (2) the elite preserving strategy contributes to maintaining the correct programs and the program size restriction prevents the ineffective instructions; and (3) the gradient fitness calculation can correctly evaluate the multiple outputs programs; and (4) the above three methods, however, are difficult to generate the shortest size programs such as sharing instructions with different calculations.