Constrained optimization problems are very important and frequently appear in the real world.
The alpha constrained method is a new transformation method for constrained optimization.
In this method, a satisfaction level for the constraints is introduced, which indicates how well a search point satisfies the constraints.
The alpha level comparison, which compares search points based on their level of satisfaction of the constraints, is also introduced.
The alpha constrained method can convert an algorithm for unconstrained problems into an algorithm for constrained problems by replacing ordinary comparisons with the alpha level comparisons.
In this work, the alpha constrained genetic algorithm, which is the combination of the alpha constrained method and genetic algorithm with extended intermediate recombination, is reconsidered.
Recently, new crossover operation, Simplex Crossover, which is a very efficient and robust crossover operator, is proposed.
We propose to incorporate the simplex crossover into the alpha constrained genetic algorithm.
The effectiveness of the alpha constrained genetic algorithm with the simplex crossover is shown by comparing the algorithm with GENOCOP5.0, which is known as an efficient algorithm for constrained optimization.