The two-branch tournament genetic algorithm is presented as an approach to
determine a set of Pareto-optimal solutions to multiobjective design proble
ms. Because the genetic algorithm searches using a population of points rat
her than using a point-to-point search, it is possible to generate a large
number of solutions to multiobjective problems in a single run of the algor
ithm. The two-branch tournament and its implementation in a genetic algorit
hm (GA) to provide these solutions are discussed. This approach differs fro
m most traditional methods for GA-based multiobjective design; it does not
require the nondominated ranking approach nor does it require additional fi
tness manipulations. A multiobjective mathematical benchmark problem and a
10-bar truss problem were solved to illustrate how this approach works for
typical multiobjective problems. These problems also allowed comparison to
published solutions. The two-branch GA was also applied to a problem combin
ing discrete and continuous variables to illustrate an additional advantage
of this approach for multiobjective design problems. Results of all three
problems were compared to those of single-objective approaches providing a
measure of how closely the Pareto-optimal set is estimated by the two-branc
h GA. Finally, conclusions were made about the benefits and potential for i
mprovement of this approach.