Evolutionary Programming: An Efficient Alternative to Genetic Algorithms for Electromagnetic Optimization Problems.
Evolutionary algorithms, such as genetic algorithms (GAs) [1], evolutionary programming (EP) [2], and evolutionary strategies (ES), have recently received much attention for global optimization of electromagnetic problems [3-5,7]. These evolutionary algorithms are heuristic population-based search p...
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1998
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Evolutionary Programming: An Efficient Alternative to Genetic Algorithms for Electromagnetic Optimization Problems. Chellapilla, Kumar. Hoorfar, Ahmad. Evolutionary algorithms, such as genetic algorithms (GAs) [1], evolutionary programming (EP) [2], and evolutionary strategies (ES), have recently received much attention for global optimization of electromagnetic problems [3-5,7]. These evolutionary algorithms are heuristic population-based search procedures that incorporate random variation and selection. Of the three paradigms GAs are well known to the electromagnetic community. Even though several successful applications have been reported, recent research has identified some inefficiencies in GA performance [6]. This degradation in efficiency is apparent in applications with highly epistatic objective functions, i.e., where the parameters being optimized are highly correlated. On the other hand, EP and ES are more robust to epistatic objective functions and coordinate rotations. EP has been shown to be more efficient than GA on many function optimization problems [2]. The dynamics of GA are explained through the building block hypothesis and the schema theorem [1], which are not fully accepted in the evolutionary computation literature. On the other hand, the convergence theory for EP is well established and EP has been proven to asymptotically converge to the global optimum with probability one, under elitist selection [2]. Further, as will be demonstrated, EP is well suited for optimizing continuous, discrete, and mixed parameter optimization problems. Binary GAs require the parameters to be coded as bits. The selection of the crossover and mutation probabilities is quite arbitrary and they are not adapted during evolution. The selection of the initial values for the strategy parameters for EP and ES are well defined and efficient adaptive and self-adaptive techniques exist for adapting these parameters during evolution. In this work, the capabilities of EP will be demonstrated and contrasted with those obtained using GAs on three challenging electromagnetic optimization problems, namely, the design of optimally thinned linear arrays, aperiodic arrays, and Yagi-Uda antennas. A more complicated problem on the gain optimization of a multilayered microstrip Yagi array will be discussed in a separate paper in this symposium [8]. 1998 Villanova Faculty Authorship vudl:176980 IEEE Transactions on Antennas and Propagation, 1998. en |
| dc.title_txt_mv |
Evolutionary Programming: An Efficient Alternative to Genetic Algorithms for Electromagnetic Optimization Problems. |
| dc.creator_txt_mv |
Chellapilla, Kumar. Hoorfar, Ahmad. |
| dc.description_txt_mv |
Evolutionary algorithms, such as genetic algorithms (GAs) [1], evolutionary programming
(EP) [2], and evolutionary strategies (ES), have recently received much attention
for global optimization of electromagnetic problems [3-5,7]. These evolutionary
algorithms are heuristic population-based search procedures that incorporate random variation
and selection. Of the three paradigms GAs are well known to the electromagnetic
community. Even though several successful applications have been reported, recent
research has identified some inefficiencies in GA performance [6]. This degradation in
efficiency is apparent in applications with highly epistatic objective functions, i.e., where
the parameters being optimized are highly correlated. On the other hand, EP and ES are
more robust to epistatic objective functions and coordinate rotations. EP has been shown
to be more efficient than GA on many function optimization problems [2].
The dynamics of GA are explained through the building block hypothesis and the
schema theorem [1], which are not fully accepted in the evolutionary computation literature.
On the other hand, the convergence theory for EP is well established and EP has been
proven to asymptotically converge to the global optimum with probability one, under elitist
selection [2]. Further, as will be demonstrated, EP is well suited for optimizing continuous,
discrete, and mixed parameter optimization problems. Binary GAs require the
parameters to be coded as bits. The selection of the crossover and mutation probabilities is
quite arbitrary and they are not adapted during evolution. The selection of the initial values
for the strategy parameters for EP and ES are well defined and efficient adaptive and
self-adaptive techniques exist for adapting these parameters during evolution.
In this work, the capabilities of EP will be demonstrated and contrasted with those
obtained using GAs on three challenging electromagnetic optimization problems, namely,
the design of optimally thinned linear arrays, aperiodic arrays, and Yagi-Uda antennas. A
more complicated problem on the gain optimization of a multilayered microstrip Yagi
array will be discussed in a separate paper in this symposium [8]. |
| dc.date_txt_mv |
1998 |
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Villanova Faculty Authorship |
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vudl:176980 |
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IEEE Transactions on Antennas and Propagation, 1998. |
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en |
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Chellapilla, Kumar. Hoorfar, Ahmad. |
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Chellapilla, Kumar. Hoorfar, Ahmad. Evolutionary Programming: An Efficient Alternative to Genetic Algorithms for Electromagnetic Optimization Problems. |
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Chellapilla, Kumar. Hoorfar, Ahmad. |
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IEEE Transactions on Antennas and Propagation, 1998. |
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Villanova Faculty Authorship |
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Chellapilla, Kumar. |
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1998 |
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Evolutionary Programming: An Efficient Alternative to Genetic Algorithms for Electromagnetic Optimization Problems. |
| description |
Evolutionary algorithms, such as genetic algorithms (GAs) [1], evolutionary programming
(EP) [2], and evolutionary strategies (ES), have recently received much attention
for global optimization of electromagnetic problems [3-5,7]. These evolutionary
algorithms are heuristic population-based search procedures that incorporate random variation
and selection. Of the three paradigms GAs are well known to the electromagnetic
community. Even though several successful applications have been reported, recent
research has identified some inefficiencies in GA performance [6]. This degradation in
efficiency is apparent in applications with highly epistatic objective functions, i.e., where
the parameters being optimized are highly correlated. On the other hand, EP and ES are
more robust to epistatic objective functions and coordinate rotations. EP has been shown
to be more efficient than GA on many function optimization problems [2].
The dynamics of GA are explained through the building block hypothesis and the
schema theorem [1], which are not fully accepted in the evolutionary computation literature.
On the other hand, the convergence theory for EP is well established and EP has been
proven to asymptotically converge to the global optimum with probability one, under elitist
selection [2]. Further, as will be demonstrated, EP is well suited for optimizing continuous,
discrete, and mixed parameter optimization problems. Binary GAs require the
parameters to be coded as bits. The selection of the crossover and mutation probabilities is
quite arbitrary and they are not adapted during evolution. The selection of the initial values
for the strategy parameters for EP and ES are well defined and efficient adaptive and
self-adaptive techniques exist for adapting these parameters during evolution.
In this work, the capabilities of EP will be demonstrated and contrasted with those
obtained using GAs on three challenging electromagnetic optimization problems, namely,
the design of optimally thinned linear arrays, aperiodic arrays, and Yagi-Uda antennas. A
more complicated problem on the gain optimization of a multilayered microstrip Yagi
array will be discussed in a separate paper in this symposium [8]. |
| title |
Evolutionary Programming: An Efficient Alternative to Genetic Algorithms for Electromagnetic Optimization Problems. |
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Evolutionary Programming: An Efficient Alternative to Genetic Algorithms for Electromagnetic Optimization Problems. |
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Evolutionary Programming: An Efficient Alternative to Genetic Algorithms for Electromagnetic Optimization Problems. |
| title_full_unstemmed |
Evolutionary Programming: An Efficient Alternative to Genetic Algorithms for Electromagnetic Optimization Problems. |
| title_short |
Evolutionary Programming: An Efficient Alternative to Genetic Algorithms for Electromagnetic Optimization Problems. |
| title_sort |
evolutionary programming: an efficient alternative to genetic algorithms for electromagnetic optimization problems. |
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1998 |
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