Introducing Modularity and Homology in Grammatical Evolution to Address the Analog Electronic Circuit Design Problem

Abstract

We present a new approach based on grammatical evolution (GE) aimed at addressing the analog electronic circuit design problem. In the new approach, called multi-grammatical evolution (MGE), a chromosome is a variable-length codon string that is divided into as many partitions as subproblems result from breaking down the original optimization problem: circuit topology and component sizing in our case. This leads to a modular approach where the solution of each subproblem is encoded and evolved in a partition of the chromosome. Additionally, each partition is decoded according to a specific grammar and the final solution to the original problem emerges as an aggregation result associated with the decoding process of the different partitions. Modularity facilitates the encoding and evolution of the solution in each subproblem. On the other way, homology helps to reduce the potentially destructive effect associated with standard crossover operators normally used in GE-based approaches. Seven analog circuit designs are addressed by an MGEbased method and the obtained results are compared to those obtained by different methods based on GE and other evolutionary paradigms. A simple parsimony mechanism was also introduced to ensure compliance with design specifications and reduce the number of components of the circuits obtained. We can conclude that our method obtains competitive results in the seven circuits analyzed.

Analog Electronic Circuit Synthesis Using Simulated Annealing and Geometric Circuit Evolution

“…The results of the ACID-GE [10] and ACID-MGE [12] algorithms were used to compare with those in this work, with a focus in the ACID-MGE that presents a better performance. Data from the results of these two algorithms were obtained from tables 4, 5 and 7 of [12].…”

“…In order to test the SANN-GCE, it was selected seven problems (test circuits) as use cases for the algorithm. These test circuits are the same as in [10] and [12], because they presented seven different use cases with detailed results, and all tests were run 50 times, which allowed us to make a robust comparison.…”

“…The solutions are then evolved using a genetic algorithm. In another article, the authors presented the Multi Grammatical Evolution (MGE) [12]. It separated the original problem into two distinct sub-problems: component sizing and circuit topology.…”

“…The main contributions of this article are: (1) we show how this novel algorithm (GCE) combined with classic simulated annealing can be used to successfully synthesize analog electronic circuits using a simpler mutation-only approach, evolving one solution at time (in contrast with population based algorithms, e.g. Genetic Algorithm); (2) its performance, when compared with a benchmark [12], has significant advantages in some key metrics: (2.1) it is 14.17× faster; (2.2) the fitness of the best synthesized circuits is 15.88× lower (better); (2.3) the fitness standard deviation of the best obtained circuits is 6.72× lower.…”