Balance recovery from perturbation plays a crucial role in preventing people from falling during walking. Understanding the underlying mechanism of balance recovery during walking could not only bring new insights into the motor control field, but also benefit the development of walking assist systems for daily living environment, where perturbations to walking are frequently caused by slips, uneven terrain, slopes, obstacles, etc.It is evident that humans can cope with such perturbations, especially when the perturbations cannot be predicted or perceived in advance, by means of reflexes, which cause relatively fixed, unconscious muscular response patterns to perturbations within a short period of time ranging from several tens of ms to 200 ms. However, except for several hypotheses about the underlying neural mechanisms of the reflexes during walking, there is no widely accepted unified theory.In our previous study, a muscular-reflexive pattern was defined using muscle activity recorded during reflexive responses to slip perturbation. This is one important step toward the understanding of the underlying mechanism, since the pattern could serve as the quantitative target in pursuit of the underlying mechanism. We can speculate that this pattern is the optimal balance recovery behavior to human muscular-skeletal system, as a result of long evolution; however, before we use this target to guide our pursuit, we should first prove its optimality, while making clear the objective functions for balance recovery and the effect of morphological factors.Our approach includes (1) defining objective functions for balance recovery from a slip perturbation during walking; (2) using a bio-mimetic human walking simulator to perform evaluation according to the objective functions; (3) employing a genetic algorithm (GA) to search the optimal solutions, for different objective functions.Results showed that the muscular-reflexive pattern is optimal to the balance recovery from slip perturbation during walking, in terms of a hybrid evaluation measure (HEM), which takes both static and dynamic aspects of balance recovery into consideration.