Conclusion
Genetic algorithms, evolutionary algorithms

Following a surge of interest in the 1990s and 2000s, the field of evolutionary algorithms remains highly active.

• At the theoretical level, substantial work, primarily on the convergence of these algorithms through Markov chain modelling, has established solid foundation for techniques that were initially criticised for their empirical nature. These approaches provide a rich theoretical framework for refining analysis of convergence behaviour and algorithmic efficiency.

• In terms of applications, evolutionary algorithms are now widely used in both research and industrial contexts.

From an experimental standpoint, they are particularly effective for exploring difficult, irregular, high-dimensional search spaces while limiting the risk of premature convergence. Conversely, given their computational cost, it is inefficient to apply genetic algorithms...