Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Cost optimization of feed mixes by genetic algorithms
Advances in Engineering Software
Multiobjective evolutionary algorithms for multivariable PI controller design
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Comparison of design concepts in multi-criteria decision-making using level diagrams
Information Sciences: an International Journal
The smart normal constraint method for directly generating a smart Pareto set
Structural and Multidisciplinary Optimization
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This paper presents the resolution of multiobjective optimization problems as a tool in engineering design. In the literature, the solutions of this problems are based on the Pareto frontier construction. Therefore, substantial efforts have been made in recent years to develop methods for the construction of Pareto frontiers that guarantee uniform distribution and exclude the non-Pareto and local Pareto points. The normalized normal constraint is a recent contribution that generates a well-distributed Pareto frontier. Nevertheless, these methods are susceptible of improvement or modifications to obtain the same level of results more efficiently. This paper proposes a modification of the original normalized normal constraint method using a genetic algorithms in the optimization task. The results presented in this paper show a suitable behavior for the genetic algorithms method compared to classical Gauss-Newton optimization methods which are used by the original normalized normal constraint method.