In this work, the authors propose a combined approach based on a controller architecture that is able to
generate locomotion for a quadruped robot and a global optimization algorithm to generate head movement
stabilization. The movement controllers are biologically inspired in the concept of Central Pattern Generators
(CPGs) that are modelled based on nonlinear dynamical systems, coupled Hopf oscillators. This approach
allows for explicitly specified parameters such as amplitude, offset and frequency of movement and to smoothly
modulate the generated oscillations according to changes in these parameters. The overall idea is to generate
head movement opposed to the one induced by locomotion, such that the head remains stabilized. Thus,
in order to achieve this desired head movement, it is necessary to appropriately tune the CPG parameters.
Three different global optimization algorithms search for this best set of parameters. In order to evaluate
the resulting head movement, a fitness function based on the Euclidean norm is investigated. Moreover, a
constraint-handling technique based on tournament selection was implemented