In this study, alpha constrained genetic algorithm (aGA) 
which solves constrained optimization problems is proposed.
The constrained optimization problems, where the objective functions 
are minimized under given constraints, are very important and 
frequently appear in the real world.
Recently, the researches on constrained optimization using genetic 
algorithm (GA) have been widely carried out, and the results of them are 
equivalent to those by existing mathematical methods.
aGA is a method which combines alpha constrained method with GA.
In the alpha constrained method, the satisfaction level of constraints 
to express how much a search point satisfies the constraints is introduced.
The alpha level comparison which compares the search points based on 
the satisfaction level of constraints is also introduced.
  The alpha constrained method can convert constrained problems 
to unconstrained problems using alpha level comparison.
In aGA, the individuals who satisfy the constraints will evolve 
to optimize the objective function and the individuals who don't 
satisfy the constraints will evolve to satisfy the constraints, naturally.  
In this paper, the effectiveness of aGA is shown by comparing aGA 
with GENOCOP5.0 on various types of test problems, such as a linear 
programming problem, nonlinear programming problems, and problems 
with non-convex constraints.