Genetic Algorithms in Electromagnetics

By Randy L. Haupt, Douglas H. Werner. 2007

321 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/af517bde4ac6f1c4322ffba307174be98379717e

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In Artificial Intelligence

Citations in the corpus

Unconventional Phased Array Architectures and Design Methodologies—A Review (2016, methodology): “To address this problem, the use of optimization techniques based on genetic algorithms [6], [44], [45], simulated annealing (SA) [46]–[48], and particle swarm optimization (PSO) [49] have been proposed where the synthesis has been cast as the minimization of a suitably defined cost function [e.”
Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics (2010, methodology): “Introduction Nature-inspired techniques such as the Genetic Algorithm (GA) [1], Ant Colony Optimization (ACO) [2], and Particle Swarm Optimization (PSO) [3] have been shown to be some of the most effective global optimization strategies.”
The Wind Driven Optimization Technique and its Application in Electromagnetics (2013, background): “Since the early 1970s, various nature-inspired optimization algorithms have emerged starting with the Genetic Algorithm (GA) [1], some of which have proven to be very efficient global optimization methods.”
ADS-Based Guidelines for Thinned Planar Arrays (2010, background): “[8], [12], simulated annealing [13]–[17], and particle swarm optimizers [18].”
Antenna Current Optimization using MATLAB and CVX (2016, methodology): “Metaheuristic algorithms, such as genetic algorithms [44], particle swarm and gradient-based algorithms dominate the antenna optimization field due to the inherent complexity of antenna design problems.”