Research on adaptive human-machine interface layout optimization model based on reinforcement learning and genetic algorithm

Abstract With the rapid development of intelligent systems, the optimization of human-machine interface (HMI) layout has become a key issue for enhancing user experience and operational efficiency. Traditional layout optimization methods rely on manual design or static rules, which makes them difficult to adapt to diverse user needs and dynamic task scenarios. Therefore, this study proposes an adaptive human-machine interface layout optimization model that combines reinforcement learning (RL) and genetic algorithm (GA) techniques, aiming to dynamically adjust the layout of interface elements through intelligent algorithms to meet individual needs. Firstly, the model uses a genetic algorithm to generate the initial layout population and evaluates the rationality of the layout using a fitness function. Then, the Q-learning algorithm in reinforcement learning is introduced to dynamically optimize the layout strategy based on user interaction data, thereby achieving a balance between exploration and exploitation. In the experiment, 50 users were selected to participate in the test, representing different age groups and operating habits, and the performance of traditional static layouts and optimization models was compared. The results show that the optimization model reduces task completion time by 23.7%, improves user satisfaction by 18.4%, and decreases the layout adjustment response time to an average of 1.2 s. The layout stability of the model in complex task scenarios reaches 92.3%, which is significantly better than the single algorithm-driven solution. The research demonstrates that the hybrid strategy of reinforcement learning and genetic algorithms can effectively address the dynamic adaptation problem of human-machine interface layout, providing a new approach for intelligent interaction design.