Genetic algorithms with adaptive parameters for phased array synthesis

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. The 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; (2) the need to tune parameters by hand (generally done by repeatedly running the code with different parameter choices) is reduced.