Humans appear to solve complex vision tasks in an almost effortless manner, as compared to their computer counterparts. One major reason for this is the intelligent cooperation between the sensory and the motor system, which is facilitated by development of motor skills that help to shape visual information that is relevant to a specific vision task. This dynamic interaction of sensory-motor components in biological systems can be a great inspiration to how artificial systems, such as robots could use their visual mechanism to interacts with their world. In this thesis, we seek to explore an approach to active visioninspired by biological evolution, which does not use a predefined framework or assumptions, but develops motor strategies for a given task through progressive adaptation of the evolutionary method. Thus, this kind of approach will give freedom to artificial systems in the discovery of eye movement strategies that may be useful to biological systems but are not known to us. The contributions of this thesis are: 1. We used this type of active vision system for more complex images taken from the camera of the iCub robot.2. We demonstrated the effectiveness of the active vision system in a more realistic setting for 3D object categorisation using the humanoid robot (iCub) platform.3. We extended the applicability of the system to the 3D environment for indoor and outdoor environment classification task using the iCub platform.4. We extended the system with pre-processing using Uniform Local Binary Patterns [1] in both 2D and 3D environment categorisation tasks.5. We further extended the system with pre-processing using Histogram of Oriented Gradients [2] for classification tasks in the 2D and 3D environments.Analysis of the results from the system shows that the model was able to complete discrimination tasks through: (i) exploiting sensory-motor coordination to experience sensory stimuli that facilitates the classification tasks; (ii) an indication of integration of perceptual information over time.