Optimization of thinned arrays using stochastic Immunity Genetic Algorithm

In this paper we propose a novel genetic algorithm called immunity genetic algorithm (IGA) based on stochastic crossover evolution to solve the synthesis problem of thinned arrays. Our crossover operator is a variant of the known GA operator. A new expression of the array factor for a specific number of elements N is expressed as a linear discrete cosine transform (DCT). Using IGA to generate thousands of array bit patterns and the DCT to compute the fitness function will result in a very high speed computation compared to traditional computation techniques. This high performance allows us to find a good approximation of the absolute minimum SLL of synthesized thinned arrays. Simulation results of this novel array signal processing technique show the effectiveness for pattern synthesis with low SLL.