6. Conclusion

After a strong fad in the 1990-2000s, the field of evolutionary algorithms remains remarkably active.

At the theoretical level: significant work, mainly on the convergence of these algorithms (Markov chain modeling), has laid a solid foundation for these techniques, which were initially criticized for their "empirical" aspect. These approaches provide a rich theoretical framework for refining many convergence and efficiency analyses.
Applications: these algorithms are widely used in research and industry.

From an experimental point of view, these algorithms are effective for searching in difficult and irregular multidimensional spaces, limiting the risk of premature convergence. On the other hand, given their computational cost, it is inefficient to attempt to apply GAs in cases where techniques...

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Conclusion

Bibliography

(1) - ALTENBERG (L.) - Evolutionary Computation Models from Population Genetics, Part 2: An Historical Toolbox, - in Congress on Evolutionary Computation (2000).
(2) - ANGELINE (P.J.), POLLACK (J.B.) - Competitive Environments Evolve Better Solutions...