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The Assembly Line Balancing (ALB) problem is a well-known manufacturing optimization problem, which determines the assignment of various tasks to an ordered sequence of stations, while optimizing one or more objectives without violating restrictions imposed on the line. As Genetic Algorithms (GAs) have established themselves as a useful optimization technique in the manufacturing field, the application of GAs to ALB problem has expanded a lot. This paper describes a generalized Pareto-based scale-independent fitness function (gp-siffGA) for solving ALB problem with worker allocation (ALB-wa) to minimize the cycle time, the variation of workload and the total cost under the constraint of precedence relationships at the same time. For this approach, first a random key-based representation method adapting the GA was proposed. Following, advanced genetic operators adapted to the specific chromosome structure and the characteristics of the ALB-wa problem were used. Moreover, Pareto dominance relationship was used to solve the ALB-wa problem without using relative preferences of multiple objectives. Finally, the performance of proposed method was validated through numerical experiments. The results indicated that the proposed approach improved the quality of solutions more than the other existing GA approaches.