Examples of application
Genetic and evolutionary algorithms

EA covers an extremely wide range of applications and is of interest to researchers and engineers from a wide variety of disciplines.

• In numerical optimization: when the functions to be optimized are complex, large-scale, irregular, or poorly understood.

• In combinatorial optimization: for problems in graph theory (traveling salesman, graph coloring), task sequencing, resource allocation, scheduling, and knapsack problems, whose efficient resolution is most often based on AEs hybridized with "classical" techniques. It is in this context that numerous studies have been conducted on the representation of problems and the handling of constraints.

• In artificial intelligence and cognitive science: where we tend to exploit the adaptive capabilities of AEs in combination with learning...