Genetic algorithm for constrained global optimization in continuous variables

摘要

We present a stochastic global optimization algorithm, referred to as a Genetic Algorithm (GA), for solving constrained optimization problems over a compact search domain. It is a real-coded GA that converges in probability to the optimal solution. The constraints are treated through a repair operator. A specific repair operator is included for linear inequality constraints.