IEEE Antennas and Wireless Propagation Letters · 2016 · 35 citations · 3 references
ArchitectureComputational EngineeringMaterials EngineeringComputational ElectromagneticsDesign OptimizationOptimizationEvolutionary ComputationEvolutionary OptimizationGenetic AlgorithmsGenetic ProgrammingMetaheuristic OptimizationPopulation-based SearchComputational EfficiencyComputational Efficiency AnalysisEfficiencyEvolutionary AlgorithmsGenetic Algorithm OptimizationGenetic Algorithms For DesignGenetic Programming For DesignPopulation Based Design Optimization
The computational efficiency of existing genetic programming (GP) software is improved through the addition of parallelization and hybridization with a low-level optimizer. The low-level optimizer is implemented using genetic algorithm, and two different versions of the developed hybrid GP program are presented. Timing and efficiency performance are discussed, which includes a method of making the original optimizer more than eight times faster. Efficiency results indicate that through the use of the optimizer, with sufficient variables included, significantly fewer (about ¼ to ½) GP generations are required to achieve working metamaterial designs. Two low-frequency broadband (225-450 MHz) ground plane design examples, which are 25.3% thinner than a previously published design covering the same frequency band, are included.
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