>>I fully anticipate they will come back in fashion.
It's not possible to predict fashions, but I seriously doubt that the whole evolutionary algorithms field will see a resurgence based on anything other than buzzword-driven fads.
In general evolutionary algorithms perform terribly poorly in any optimization problem, and at best are effective (but not efficient) in exploring problems involving the arbitrary addition/removal of dimensions/parameters, including high-dimensional discrete optimization problems.
Given there are already techniques to convert discrete optimization problems to non-discrete ones and to add/remove arbitrary parameters, and there are already global optimization techniques which are far more efficient and, more importantly, deterministic, genetic algorithms don't really offer any relevant advantage over even brute-force techniques.
It's not possible to predict fashions, but I seriously doubt that the whole evolutionary algorithms field will see a resurgence based on anything other than buzzword-driven fads.
In general evolutionary algorithms perform terribly poorly in any optimization problem, and at best are effective (but not efficient) in exploring problems involving the arbitrary addition/removal of dimensions/parameters, including high-dimensional discrete optimization problems.
Given there are already techniques to convert discrete optimization problems to non-discrete ones and to add/remove arbitrary parameters, and there are already global optimization techniques which are far more efficient and, more importantly, deterministic, genetic algorithms don't really offer any relevant advantage over even brute-force techniques.