Structured Graph Generation by Evolutionary Algorithm for Program Code Development

Understanding and interpreting complex coupled systems remains one of the biggest challenges in the world. Examples of these applications range from industrial manufacturing to the temporal characteristics of real-world conditions. To address this challenge, this paper presents a novel approach to structured program code development based on graph generation. The problem is considered from the perspective of an inductive link prediction problem structured by an evolutionary algorithm. The self-adaptation of relevant knowledge takes place in a closed loop, where systematic relationships are constantly improved and extended. The required system behaviour is mapped step by step, taking into account constraints, limitations and expert knowledge. Structured graph generation is used to represent logic functions and interpret complex coupled relationships. The presented strategy enables targeted plant control through customised program code concepts, which are used to optimise processes and increase efficiency. The approach is validated through the design of interpretable programmable control logic on an industrial manufacturing process and obtain a comparable solution to the work of a trained professional. The achieved results demonstrate the next level of independent self-optimisation in learning and interpreting logical relationships in automation.