This paper presents the proposal of using two bio-inspired metaheuristics-genetic algorithms (GAs) and particle swarm optimization (PSO)-to adjust the free coefficients of a Gaussian adaptive proportional-integral-derivative (GAPID) controller. When a specific adaptation rule is imposed to a conventional proportional-integral-derivative (PID) controller, either by means of a hyperbolic tangent function or a Gaussian function, the solution is left exposed to the function restrictions/impositions. Finding the correct proportionality between the parameters is an arduous task, which often does not have an algebraic solution. Each Gaussian function of each control action has three parameters, resulting in a total of nine parameters to be defined. This paper proposes making the parameters linked to the linear PID gains, in order to keep the GAPID the same design requirements as for the PID. Then, two metaheuristics (GA and PSO) were employed in order to find the best parameters for the GAPID. A comparison between these two strategies is presented. In this investigation, a well-known plant of a step-down dc-dc converter is used, which represents a typical second-order system, where the absence of significant nonlinearities helps focus the study on the control behavior. Simulation and experimentation were performed, and both have been successful, but PSO stood out due to its simplicity and low-computational effort.