The aim of this study is to investigate the behaviour and application of an evolutionary algorithm (EA) based on a particular approach of cooperative co-evolution algorithm (CCEA), the Parisian Approach. It evolves and keeps an entire population as an optimal solution to the problem instead of keeping only the best individual in classical EAs. The CCEA we selected is called the “Fly algorithm”. It is named after flies, because the individuals are extremely primitive and correspond to three-dimensional (3-D) points. This algorithm has been relatively overlooked despite showing promising results in real-time robotic and image reconstruction in tomography. Our focus in this study is on two types of applications: medical imaging and digital art. i) In the medical application, we aim to improve quantitative results in 3-D reconstructed volumes in positron emission tomography (PET).We investigate the use of density fields, based on Metaballs and on Gaussian functions respectively, to obtain a realistic output. We also investigate how to exploit individuals’ fitness to modulate their individual footprint in the final reconstructed volume. An individual’s fitness can be seen as a level of confidence in its 3-D position. The resulting volumes are compared with previous work in terms of normalised-cross correlation. In our test cases, data fidelity increases by more than 10% when density fields are used instead of using a naive approach. Our method also provides reconstructions comparable to those obtained using well-established techniques used in medicine (e.g., filtered back-projection (FBP) and ordered subset expectation-maximization (OSEM)). Our algorithm relies heavily on the mutation operator. We propose 4 different fully adaptive mutation operators: basic mutation, adaptive mutation variance, dual mutation and directed mutation. Their impact on the algorithm efficiency is analysed and validated on PET reconstruction. ii) In the digital art application, we present the first application of the Fly algorithm in digital art. This branch of digital art is called “evolutionary art”. The motivation is to evaluate the algorithm with a much more complex structure of flies. They are still defined as simplistic primitives (3-D points) but with colours, sizes and rotations. Different visual effects were investigated, such as mosaic-like images and spray paint rendering. An online survey (including 41 participates) was conducted to validate our approach. Participants compared our results with similar ones generated with open-source software (GIMP). Again, our method shows promising results. In conclusion, our investigations confirm that the Fly algorithm works well with a complex search space. We demonstrate a fast and accurate solution to optimise a set of parameters in both applications. The Fly algorithm can improve reconstructed image quality compared to FBP and OSEM in medical application and to GIMP in digital art application.