Affiliated with the
Communication & Space
Sciences Laboratory

Advanced Optimization and Inverse Design in EM

Over the years engineers have often looked to nature for inspiration when seeking new and innovative ways to solve complex design problems. For instance, the development of fractal geometry was originally inspired by studying the shapes of natural objects such as trees, leaves, ferns, terrain, coastlines, snowflakes, and cloud boundaries. Neural Networks (NN) have been developed to mimic the human decision-making process. Similarly, Genetic Algorithms (GA) are based on the Darwinian notion of survival of the fittest and evolution, while Particle Swarm Optimization (PSO) is based on the theory of swarming insects or flocking birds. In fact the origin of several commonly used analysis techniques of modern day electromagnetics may be traced to processes found in the natural world. Researchers at the Pennsylvania State University CEARL are internationally recognized for their work on the development and application of powerful nature-based antenna design techniques that incorporate aspects of fractals, NN, GA, and/or PSO.


Genetic Algorithms are an optimization procedure based on the mechanics of natural selection (i.e., survival of the fittest) and genetics

  <> Children inherit traits from their parents and, as a result, resemble them in some fashion. This is achieved in GA via crossover and mutation operators.

<> Survival is based on the fitness of the individual, so that as time progresses there is an evolution in the genetic composition of individuals

<> The Genetic Algorithm can be viewed as a method for distilling good traits from a population of individuals, and recombining them to achieve a goal

Effective when finding a global minimum in a high-dimension, multimodal function domain.