Optimization Method based on Genetic Algorithms

The design of electromagnetic systems using methods of optimization have been carried out with deterministic methods. However, these methods are not efficient, because the object functions obtained from electromagnetic optimization problems are often highly non-linear, stiff, multiextreme and non-differential. The lack of a single method available to deal with multidimensional problems, including those with several goals to optimize, has generated the need to use numerical processes for optimization. This paper presents a method of global optimization based on genetic algorithms. The Genetic Algorithms are a versatile tool, which can be applied as a global optimization method to problems of electromagnetic engineering, because they are easy to implement to non-differentiable functions and discrete search spaces. It is also shown how, in some cases, genetic algorithms have been applied with success in electromagnetic problems, such as antenna design, far-field prediction, absorber coatings design, etc.

[1]  The Writer OF The Article,et al.  The Origin of Species , 1871, Nature.

[2]  R. Punnett,et al.  The Genetical Theory of Natural Selection , 1930, Nature.

[3]  R. A. Fisher,et al.  The Genetical Theory of Natural Selection , 1931 .

[4]  Hans-Paul Schwefel,et al.  Numerical Optimization of Computer Models , 1982 .

[5]  Constantine A. Balanis,et al.  Antenna Theory: Analysis and Design , 1982 .

[6]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[8]  D. E. Goldberg,et al.  Genetic Algorithms in Search, Optimization & Machine Learning , 1989 .

[9]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[10]  Kenneth A. De Jong,et al.  An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms , 1990, PPSN.

[11]  Dragan Cvetkovic,et al.  The Optimal Population Size for Uniform Crossover and Truncation Selection , 1994 .

[12]  Randy L. Haupt Thinned arrays using genetic algorithms , 1994 .

[13]  Hao Wang,et al.  Introduction to Genetic Algorithms in Electromagnetics , 1995 .

[14]  E Theodor,et al.  The origin of the species. , 1995, M.D. computing : computers in medical practice.

[15]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

[16]  David E. Goldberg,et al.  Genetic algorithm design of Pareto optimal broadband microwave absorbers , 1996 .

[17]  Thomas Bäck,et al.  Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .

[18]  Jr. Allen B. Tucker,et al.  The Computer Science and Engineering Handbook , 1997 .

[19]  Yahya Rahmat-Samii,et al.  Electromagnetic Optimization by Genetic Algorithms , 1999 .

[20]  Robert I. Damper,et al.  Towards the evolutionary emergence of increasingly complex advantageous behaviours , 2000, Int. J. Syst. Sci..

[21]  M. Ribo,et al.  A genetic algorithm based method for source identification and far-field radiated emissions prediction from near-field measurements for PCB characterization , 2001 .

[22]  E. Altshuler Electrically small self-resonant wire antennas optimized using a genetic algorithm , 2002 .

[23]  Giulio Antonini,et al.  A genetic optimization technique for intrinsic material properties extraction , 2002, 2002 IEEE International Symposium on Electromagnetic Compatibility.

[24]  Hao Ling,et al.  Shape optimization of corrugated coatings under grazing incidence using a genetic algorithm , 2003 .

[25]  A. Orlandi,et al.  Reconstruction of the parameters of Debye and Lorentzian dispersive media using a genetic algorithm , 2003, 2003 IEEE Symposium on Electromagnetic Compatibility. Symposium Record (Cat. No.03CH37446).

[26]  Carlos D. Toledo Genetic algorithms for the numerical solution of variational problems without analytic trial functions , 2005 .