2004IEEE Transactions on Antennas and PropagationRequires access

Comparison of Fitness Landscapes for Evolutionary Design of Dipole Antennas

Jarmo T. Alander, Lyudmila Zinchenko, S.N. Sorokin

Open publisher page 3 citations

Abstract

Evolutionary algorithms are powerful optimization and design tools, and the design of fitness function is their crucial phase. The ruggedness of a fitness function landscape has a profound effect on optimization speed. The choice of objectives and penalty coefficients are important problems for evolutionary antenna design, because these parameters influence the ruggedness of the fitness function landscape. In this paper, we analyze several fitness function landscapes for evolutionary design of dipole antennas using the method of basin evaluation. Our approach makes it possible to compare different fitness functions and thus define proper fitness functions for evolutionary antenna design.

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Evolutionary algorithms are powerful optimization and design tools, and the design of fitness function is their crucial phase. The ruggedness of a fitness function landscape has a profound effect on optimization speed. The choice of objectives and penalty coefficients are important problems for evolutionary antenna design, because these parameters influence the ruggedness of the fitness function landscape. In this paper, we analyze several fitness function landscapes for evolutionary design of dipole antennas using the method of basin evaluation. Our approach makes it possible to compare different fitness functions and thus define proper fitness functions for evolutionary antenna design.

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Available abstract

Evolutionary algorithms are powerful optimization and design tools, and the design of fitness function is their crucial phase. The ruggedness of a fitness function landscape has a profound effect on optimization speed. The choice of objectives and penalty coefficients are important problems for evolutionary antenna design, because these parameters influence the ruggedness of the fitness function landscape. In this paper, we analyze several fitness function landscapes for evolutionary design of dipole antennas using the method of basin evaluation. Our approach makes it possible to compare different fitness functions and thus define proper fitness functions for evolutionary antenna design.

Key concepts: Fitness approximation, Fitness function, Fitness landscape, Evolutionary algorithm, Computer science, Evolutionary computation, Function (biology), Antenna (radio)

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