### Approaches Based on Genetic Algorithms for Multiobjective Optimization Problems

Abstract

In engineering, it is often necessary to formulate problems in which there are
several criteria or objectives. It is unlikely that a solution that optimizes one
of the objectives will be optimal for any of the others. Compromise solutions
are therefore sought such that no other solutions are better in any one objective
while remaining no worse in the others. These types of problems are known as
either multiobjective, multicriteria, or vector optimization problems. The problem
addressed in this paper concerns the proposition of different approaches based
on Genetic Algorithms to solve multiobjective optimization problems. We use
notions about population manipulation and Pareto theory to develop our approaches,
and study the Left Ventricle 3D Reconstruction problem from two Angiographics
Views to test them.