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Toward Automated Design of Industrial-Strength Analog Circuits by Means of Genetic Programming

  • John R. Koza
  • Lee W. Jones
  • Martin A. Keane
  • Matthew J. Streeter
  • Sameer H. Al-Sakran
Part of the Genetic Programming book series (GPEM, volume 8)

Abstract

It has been previously established that genetic programming can be used as an automated invention machine to synthesize designs for complex structures. In particular, genetic programming has automatically synthesized structures that infringe, improve upon, or duplicate the functionality of 21 previously patented inventions (including six 21st-century patented analog electrical circuits) and has also generated two patentable new inventions (controllers). There are seven promising factors suggesting that these previous results can be extended to deliver industrial-strength automated design of analog circuits, but two countervailing factors. This chapter explores the question of whether the seven promising factors can overcome the two countervailing factors by reviewing progress on an ongoing project in which we are employing genetic programming to synthesize an amplifier circuit. The work involves a multiobjective fitness measure consisting of 16 different elements measured by five different test fixtures. The chapter describes five ways of using general domain knowledge applicable to all analog circuits, two ways for employing problem-specific knowledge, four ways of improving on previously published genetic programming techniques, and four ways of grappling with the multi-objective fitness measures associated with real-world design problems.

Key words

Automated design automated circuit synthesis analog circuits amplifier evolvable hardware developmental process genetic programming 

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Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • John R. Koza
    • 1
  • Lee W. Jones
    • 2
  • Martin A. Keane
    • 3
  • Matthew J. Streeter
    • 4
  • Sameer H. Al-Sakran
    • 2
  1. 1.Stanford UniversityStanford
  2. 2.Genetic Programming Inc.Mountain View
  3. 3.Econometrics Inc.Chicago
  4. 4.Carnegie Mellon UniversityPittsburgh

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