An evolutionary optimization kernel with adaptive parameters applied to analog circuit design

This paper presents a new evolutionary optimization kernel applied to the automatic synthesis of high performance analog and mixed-signal ICs. This enhanced evolutionary approach is based on a modified genetic algorithm with self adaptive parameters tailored to efficiently control the optimization process. Particularly, the benefits introduced by self adaptive parameters consist of, first, reducing the problem complexity by using a grid with an adaptive resolution or step to describe the search space, then, improving the circumvention of local minima by including an adaptive mutation operator. The enhanced optimization kernel were tested for a broad range of well known test functions and compared to other approaches using MATLAB. Finally, the proposed optimization-based approach is demonstrated for the design of high-performance differential amplifiers using HSPICE as the evaluation engine.