Novelty Search for the Synthesis of Current Followers

Abstract

A topology synthesis method is introduced using genetic algorithms (GA) based on novelty search (NS). NS is an emerging meta-heuristic, that guides the search based on the novelty of each solution instead of the objective function. The synthesized topologies are current follower (CF) circuits; these topologies are new and designed using integrated circuit CMOS technology of 0.35µm. Topologies are coded using a chromosome divided into four genes: small signal gene (SS), MOSFET synthesis gene (SMos), polarization gene (Bias) and current source synthesis gene (CM). The proposed synthesis method is coded in MatLab and uses SPICE to evaluate the CFs fitness. The GA based on NS (GA-NS) is compared with a standard objective-based GA, showing unique search dynamics and improved performance. Experimental results show twelve CFs synthesized by the GA-NS algorithm, and their main attributes are summarized and discussed. This work is the first to show that NS can be used as a promising alternative in the field of automatic circuit synthesis.

Design and Implementation of a CVRP Simulator Using Genetic Algorithms

“…Through the use of functions to read from and write to files, the file includes an identifier, coordinates x & y and the demand, the function creates a new .m extension file. We decide to use Matlab due its flexibility to code algorithms and capacity for doing simulations as shown in [18,19].…”