This paper presents an exhaustive study of the Simple Genetic Algorithm (SGA), Steady State Genetic Algorithm (SSGA) and Replacement Genetic Algorithm (RGA). The performance of each method is analyzed in relation to several operators types of crossover, selection and mutation, as well as in relation to the probabilities of crossover and mutation with and without dynamic change of its values during the optimization process. In addition, the space reduction of the design variables and global elitism are analyzed. All GAs are effective when used with its best operations and values of parameters. For each GA, both sets of best operation types and parameters are found. The dynamic change of crossover and mutation probabilities, the space reduction and the global elitism during the evolution process show that great improvement can be achieved for all GA types. These GAs are applied to TEAM benchmark problem 22.

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The authors present a procedure that permits the use of steady-state information to constrain the identification of nonlinear polynomial models. Such a procedure has three main steps. First, a general framework is provided that relates the static function of nonlinear global polynomial models to their terms and parameters. Second, using standard nonlinear programming techniques, a rational function is fitted to the system static function, which is assumed to be known and is used as auxiliary information. Finally, the information gathered in the first two steps is used to write a set of equality constraints that are exactly satisfied by a standard constrained least-squares algorithm used to estimate the parameters of the identified model. It is shown that the resulting model will always have the specified static nonlinearity and will use additional degrees of freedom to fit the dynamics underlying the observed data.

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This paper presents a multi-objective evolutionary algorithm based on decomposition (MOEA/D) to design broadband optimal Yagi-Uda antennas. A multi-objective problem is formulated to achieve maximum directivity, minimum voltage standing wave ratio and maximum front-to-back ratio. The algorithm was applied to the design of optimal 3 to 10 elements Yagi-Uda antennas, whose optimal Pareto fronts are provided in a single picture. The multi-objective problem is decomposed by Chebyshev decomposition, and it is solved by differential evolution (DE) and Gaussian mutation operators in order to provide a better approximation of the Pareto front. The results show that the implemented MOEA/D is efficient for designing Yagi-Uda antennas.

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