Hello,

I’m dealing with multi-objective optimization problem. I wonder if there is any support of genetic algorithm with Julia. Besides, compared with other solvers, does GA perform well solving MOO? For example, it cost longer or shorter time?

Does it work with JuMP.jl?

JUMP doesn’t incorporate metaheuristic algorithms like genetic algorithm, particle swarm, etc. But check out Metaheuristics.jl, which implements many different metaheuristic algorithms, including GA, and which supports multiobjective optimization.

There is a wrapper package Nonconvex.jl that includes Metaheuristics.jl and many other algorithms, but most of the others require that the derivatives of the objective function can be calculated, unlike the metaheuristic algorithms. I’m not sure if Nonconvex supports multiple objectives.

http://optimization.sciml.ai/stable/

Optimization.jl supports a ton of algorithms. In particular it has a wrapper to Metaheuristics.jl and GAs through that:

http://optimization.sciml.ai/stable/optimization_packages/metaheuristics/

Not yet