Multi-objective formulations are a realistic model for many complex engineering optimization problems. In many real-life problems, objectives under consideration conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. Due to the multiplicity in solutions, these problems were proposed to be solved suitably using evolutionary algorithms which use a population approach in its search procedure. Since Genetic Algorithm (GA) works with a set of individual solutions called population, it is natural to adopt GA schemes for Multi-Objective Optimization problems so that once can capture a number of solutions simultaneously.

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