This paper presents a tutorial and overview of genetic algorithms for electromagnetic optimization. Genetic-algorithm (GA) optimizers are robust, stochastic search methods modeled on the concepts of natural selection and evolution. The relationship between traditional optimization techniques and the GA is discussed. Step-by-step implementation aspects of the GA are detailed, through an example with the objective of providing useful guidelines for the potential user. Extensive use is made of sidebars and graphical presentation to facilitate understanding. The tutorial is followed by a discussion of several electromagnetic applications in which the GA has proven useful. The applications discussed include the design of lightweight, broadband microwave absorbers, the reduction of array sidelobes in thinned arrays, the design of shaped-beam antenna arrays, the extraction of natural resonance modes of radar targets from backscattered response data, and the design of broadband patch antennas. Genetic-algorithm optimization is shown to be suitable for optimizing a broad class of problems of interest to the electromagnetic community. A comprehensive list of key references, organized by application category, is also provided.

Genetic algorithms in engineering electromagnetics

Genetic algorithms are on the rise in electromagnetics as design tools and problem solvers because of their versatility and ability to optimize in complex multimodal search spaces. This paper describes the basic genetic algorithm and recounts its history in the electromagnetics literature. Also, the application of advanced genetic operators to the field of electromagnetics is described, and design results are presented for a number of different applications.

Genetic algorithm optimization applied to electromagnetics: a review

This paper focuses on interacting particle systems methods for solving numerically a class of Feynman-Kac formulae arising in the study of certain parabolic differential equations, physics, biology, evolutionary computing, nonlinear filtering and elsewhere. We have tried to give an “expose” of the mathematical theory that is useful for analyzing the convergence of such genetic-type and particle approximating models including law of large numbers, large deviations principles, fluctuations and empirical process theory as well as semigroup techniques and limit theorems for processes.

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Branching and interacting particle systems. Approximations of Feynman-Kac formulae with applications to non-linear filtering

This paper introduces a novel technique for efficiently combining genetic algorithms (GAs) with method of moments (MoM) for integrated antenna design and explores a two example applications of the GA/MoM approach. Integral to efficient GA/MoM integration is the use of direct Z-matrix manipulation (DMM). In DMM a "mother" structure is selected and its corresponding impedance or Z-matrix is filled only once prior to beginning the GA optimization process. The GA optimizer then optimizes the design by creating substructures of the mother structure as represented by the corresponding subsets of the original mother Z-matrix. Application of DMM with GA/MoM significantly reduces the total optimization time by eliminating multiple Z-matrix fill operations. DMM also facilitates the use of matrix partitioning and presolving to further reduce the optimization time in many practical cases. The design of a broad-band patch antenna with greater than 20% bandwidth and a dual-band patch antenna are presented as examples of the utility of GA/MoM with DMM. Measured results for the dual-band antenna are compared to numerical results. Excellent agreement between numerical and measured results is observed.

Genetic algorithms and method of moments (GA/MOM) for the design of integrated antennas

The application of modern electromagnetic theory in real world radiation and scattering problems often requires or at least benefits from the use of optimization. This paper discusses a relatively new approach to the electromagnetic optimization problem called the genetic algorithm that can handle the common optimization problem characteristics with relative ease yielding near global maxima in cases that cannot be readily handled by any other optimization method.

Genetic algorithms in electromagnetics

Synthesis of antenna patterns employing iterative optimization techniques has been studied by many authors. However, successful application of these approaches to pattern synthesis has usually been limited to relatively simple arrays or has required careful, intelligent selection of the optimization starting points dictated by the nature of the optimization techniques used and the functions being optimized. This is because conventional functional optimization techniques are either based on greedy, local optimization methods such as gradient methods or consist of random walk solution space searches. In either case, these conventional techniques are often poorly suited to the task of arbitrary pattern synthesis in 1D and 2D antenna arrays due to the high dimensional, multimodal functional domains involved. In addition, traditional optimization techniques usually require the object function to be, at the very least, continuous and, in many cases to be differentiable, placing severe limitations on the form and content of the object function. This paper presents a radically different and relatively new functional optimization methodology known as genetic algorithm (GA) optimization that overcomes the above-mentioned problems of the traditional techniques and discusses how GA optimization is applied to 1D and 2D antenna design. >

Genetic algorithm optimization and its application to antenna design

The proliferation of wireless communication networks, particularly low cost configurations, has brought with it a natural interest in network optimization. Specifically, the low cost emphasis in many modern wireless networks often places severe constraints on the acceptable level of transceiver processing circuitry complexity. Available transmitter power is also often at a premium in these modern networks. The solution to the design problem therefore usually requires significant optimization coupled with network simulation. The paper considers the synthesis of a 2-D network of N transceiver nodes with a known spatial distribution using a genetic algorithm optimization method. Each of the nodes consists of a relatively simple a transceiver (antennas, a receiver and a transmitter) in which only limited adjustments of antenna pattern and transmission power level are available. The goal of the optimization is to maximize nodal signal to noise ratios (SNR) while minimizing the transmitter power levels.

Genetic algorithm optimization of wireless communication networks

This paper introduces a novel technique for efficiently integrating genetic algorithms with method of moments (GA/MoM) for antenna design and related electromagnetic problems through the application of direct Z-matrix manipulation (DMM). In the DMM approach to GA/MoM a "mother" impedance or Z-matrix is filled only once at the beginning of the optimization process, and then the GA optimizer operates on subsets of the original "mother" Z-matrix to perform the optimization. Application of DMM to the GA/MoM significantly reduces the total optimization time by eliminating multiple Z-matrix fill operations. The DMM also facilitates the use of matrix partitioning and pre-solving to further reduce the optimization time in many practical cases.

Genetic algorithms and method of moments (GA/MoM): A novel integration for antenna design

The Wireless Adaptive Microwave Information Systems (WAMIS) program is concerned with the development of adaptive, high speed data communications systems and associated supporting technologies for mobile applications. One of the projects under the umbrella of the WAMIS program is the Hardware Technologies for Adaptive High Bit-Rate Wireless Transceivers currently underway at the University of California, Los Angeles. UCLA's project has among its goals the development of enabling technologies that facilitate the implementation of high data rate, portable digital, wireless data links with data rates up to 60 Mbit/s. The end product of UCLA's effort will be a flexible, robust, digital radio testbed enabling multimedia peer to peer, indoor communication between portable notebook style computers. The testbed radio being developed will incorporate channel coding, frequency hopping, fully adaptive antenna arrays for both transmit and receive, adaptive bit and power allocation, adaptive equalization, adaptive bit rate selection, and various custom RF and IF integrated circuits. The project goals call for the development of antenna array with a minimum operational band of 2.4 to 2.48 GHz that can be interfaced with the project hardware. These requirements imply a printed antenna with a bandwidth that is considerably wider than 2.4-2.48 GHz to allow for manufacturing tolerances and future requirements. This paper reports on an antenna array that has been developed for the UCLA WAMIS project that utilizes a newly developed, broadband, printed circuit antenna element called the tab monopole.

J.M. Johnson

Papers

Genetic algorithms in electromagnetics

Genetic algorithm optimization and its application to antenna design

Genetic algorithm optimization of wireless communication networks

Genetic algorithms and method of moments (GA/MoM): A novel integration for antenna design

Wideband tab monopole antenna array for wireless adaptive and mobile information systems application