Evolutionary circuit design: Tutorial

author = "Lukas Sekanina",

title = "Evolutionary circuit design: Tutorial",

booktitle = "13th IEEE International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS 2010)",

year = "2010",

month = "14-16 " # apr,

address = "Vienna, Austria",

pages = "5",

size = "1 page",

abstract = "Evolutionary algorithms (EAs) are population-based search algorithms that have been successfully applied to solve hard optimisation problems in many application domains. Since the early 1990's researchers have begun to apply evolutionary algorithms to synthesise electronic circuits. Nowadays it is evident that the evolutionary design approach can automatically create efficient electronic circuits in many domains. In this tutorial, fundamental concepts of evolutionary design of digital circuits are presented. In particular, the tutorial deals with Cartesian Genetic Programming (CGP) a method of genetic programming that in many cases outperforms conventional synthesis tools in terms of achievable circuit size reduction. Innovative designs will be presented in domains of small combinational circuits (where the goal is to minimise the number of gates), middle-size circuits (such as image filters intended for FPGAs where the goal is to obtain the quality of filtering of conventional methods for a significantly lower cost on a chip) and large circuits (such as benchmark circuits for comparison of testability analysis methods), covering thus circuit complexity from a few gates to millions of gates. For example, one of evolved image filters is now protected by utility model in the Czech Republic (patent pending). Evolved circuits will be compared with the best-known conventional designs. We will also show how to deal with the so-called scalability problems of evolutionary design which have been identified as the most important problems from the point of view of practical applications. In summary, tutorial participants will become familiar with the state of the art methods in the area of digital circuit evolution. They will learn how to apply CGP, construct the fitness function and run experiments.",

keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming",

DOI = "

doi:10.1109/DDECS.2010.5491830",

notes = "Also known as \cite{5491830}",