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In this paper, we present a modification of the repetitive random search algorithm. In our proposal, we change the fixed parameters which values are set by the user, by deterministic values following a chaotic map. The proposed algorithm is used to optimize four well known test functions for 10, 20 and 30 dimensions. For all cases, our proposal converges to better points that the optimal points obtained using the traditional version with fixed values in the parameters. The obtained results encourage us to continue the development and testing of the proposal algorithm with a major suite of test functions, and to compare it with other well established heuristic algorithms. [ABSTRACT FROM AUTHOR]

Evolution Strategies is a bio-inspired, robust, and efficient technique for solving optimization problems where the solution space is unrestricted. However, this assumption is unreal in many cases because the solution space is limited by complex boundaries in the form of linear and non-linear restrictions. In this paper, a modification of the original algorithm of Evolution Strategies for optimizing problems where the solution space is bounded using complex restrictions is proposed. The proposed method is based on the use of a penalization function which is zero inside of the feasible region and equal to the maximum value inside of the feasible region when an unfeasible point is considered. The proposed approach is proved using six benchmark problems. In all cases, our approach found an optimal point equal or lower than the values reported in the literature. [ABSTRACT FROM AUTHOR]

This paper describes an optimization process implemented using Genetic Algorithms. The initial population of the GA is composed of 128 chromosomes with 11 genes per chromosome. The chromosomes of the GA are composed by the length and separations of the elements of the Yagi-Uda antenna; the ranks of this genes where chosen by design standards for such antennas. All genes undergo a process of analysis to assess every one of the antennas of each generation of the GA to assign the fitness of the individuals. In order to verify the obtained results, various tests were made, and among them excel the construction of the optimized Yagi-Uda antenna to measure and verify it electromagnetic characteristics. [ABSTRACT FROM AUTHOR]