Motivated by Darwin's theories of evolution and the concept of "survival of the fittest", genetic algorithms are commonly used to solve many optimization and synthesis problems. An important issue facing the user is the selection of genetic algorithm parameters, such as mutation rate, mutation range, and number of crossovers. This paper demonstrates a genetic algorithm that simultaneously adapts these parameters during the optimization process, which is shown to outperform its best static counterpart when used to synthesize phased array weights to satisfy a specified far field sidelobe envelope. When compared to conventional static parameter implementations, computation time is saved in two ways: (1) The algorithm converges faster and (2) the need to tune parameters by hand (generally done by repeatedly running the code with different parameter choices) is reduced.
|Original language||English (US)|
|Number of pages||4|
|Journal||AP-S International Symposium (Digest) (IEEE Antennas and Propagation Society)|
|State||Published - 2003|
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering