6. Conclusion
After experiencing a strong surge in popularity in the 1990s and 2000s, the field of evolutionary algorithms remains remarkably active.
At the theoretical level: significant work, mainly concerning the study of the convergence of these algorithms (Markov chain modeling), has laid solid foundations for these techniques, which were initially criticized for their "empirical" nature. These approaches provide a rich theoretical framework that allows for the refinement of many convergence and efficiency analyses.
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At the application level: the areas of application are very diverse, and these algorithms are widely used in research and industry.
From an experimental point of view, these algorithms are effective for searching within difficult and irregular multidimensional spaces, limiting the risk of premature...
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Conclusion
Bibliography
Software tools
Inspyred, a library of bio-inspired algorithms in Python
https://pythonhosted.org/inspyred/
GAlib - C++ Genetic Algorithms Library
https://sourceforge.net/projects/galib/
Matlab Global Optimization...
Websites
Artificial Evolution Association
It brings together French researchers in this field and organizes international conferences (EA), workshops, and schools.
SIGEVO, Special Interest Group on Genetic and Evolutionary Computation
Events
ACM Genetic and Evolutionary Computation Conference (GECCO)
https://dl.acm.org/conference/gecco
IEEE Congress on Evolutionary Computation (CEC)
https://en.wikipedia.org/wiki/IEEE_Congress_on_Evolutionary_Computation
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