  Genetic algorithms, computer programs that simulate natural evolution, are
increasingly applied across many disciplines. They have been used to solve
various optimisation problems from neural network architecture search to
strategic games, and to model phenomena of adaptation and learning. Expertise
on the qualities and drawbacks of this technique is largely scattered across
the literature or former, motivating an compilation of this knowledge at the
light of the most recent developments of the field. In this review, we present
genetic algorithms, their qualities, limitations and challenges, as well as
some future development perspectives. Genetic algorithms are capable of
exploring large and complex spaces of possible solutions, to quickly locate
promising elements, and provide an adequate modelling tool to describe
evolutionary systems, from games to economies. They however suffer from high
computation costs, difficult parameter configuration, and crucial
representation of the solutions. Recent developments such as GPU, parallel and
quantum computing, conception of powerful parameter control methods, and novel
approaches in representation strategies, may be keys to overcome those
limitations. This compiling review aims at informing practitioners and
newcomers in the field alike in their genetic algorithm research, and at
outlining promising avenues for future research. It highlights the potential
for interdisciplinary research associating genetic algorithms to pulse original
discoveries in social sciences, open ended evolution, artificial life and AI.
