Systems and methods for multi-objective evolutionary algorithms with soft constraints

Systems and methods are provided to engage in multi-objective optimization where there may be one or more constraints. At least one of the constraints may be soft constraints, such that if a potential solution to the multi-objective optimization problem violates only soft constraint(s), then that potential solution may be allowed to persist in a population of potential solutions that may be used to propagate child potential solutions. Potential solutions that violate soft constraints may be tested for non-domination sorting against other potential solutions that violate soft constraints and based at least in part on values associated with the soft constraint violations.