Versatility and Population Diversity of Evolutionary Algorithms in Automated Circuit Sizing Applications

Abstract

In modern circuit design, highly specialized engineers are using computer tools to increase their chance of finding the best configurations, while decreasing the development time. However, certain tasks, like circuit sizing, consist of try and error processes that require the designer’s attention for a variable amount of time. The task duration is usually directly proportional to the complexity of the circuit. To minimize the R&D costs of the circuit, relieving the designer from the repetitive tasks is essential. Thus, the trend of replacing manual-based circuit sizing by AI solutions is growing. In this context, we are comparing the five most promising Evolutionary Algorithms for circuit sizing automation. The focus of this paper is to assess the performance of the algorithms in terms of versatility and population diversity.