Invention and creativity in automated design by means of genetic programming

Some designs are sufficiently creative that they are considered to be inventions. The invention process is typically characterized by a singular moment when the prevailing thinking concerning a long-standing problem is, in a “flash of genius,” overthrown and replaced by a new approach that could not have been logically deduced from what was previously known. This paper discusses such logical discontinuities using an example based on the history of one of the most important inventions of the 20th century in electrical engineering, namely, the invention of negative feedback by AT&T's Harold S. Black. This 1927 invention overthrew the then prevailing idiom of positive feedback championed by Westinghouse's Edwin Howard Armstrong. The paper then shows how this historically important discovery can be readily replicated by an automated design and invention technique patterned after the evolutionary process in nature, namely, genetic programming. Genetic programming employs Darwinian natural selection along with analogs of recombination (crossover), mutation, gene duplication, gene deletion, and mechanisms of developmental biology to breed an ever improving population of structures. Genetic programming rediscovers negative feedback by conducting an evolutionary search for a structure that satisfies Black's stated high-level goal (i.e., reduction of distortion in amplifiers). Like evolution in nature, genetic programming conducts its search probabilistically without resort to logic using a process that is replete with logical discontinuities. The paper then shows that genetic programming can routinely produce many additional inventive and creative results. In this regard, the paper discusses the automated rediscovery of numerous 20th-century patented inventions involving analog electrical circuits and controllers, the Sallen–Key filter, and six 21st-century patented inventions. In addition, two patentable new inventions (controllers) have been created in the same automated way by means of genetic programming. The paper discusses the promising future of automated invention by means of genetic programming in light of the fact that, to date, increased computer power has yielded progressively more substantial results, including numerous human-competitive results, in synchrony with Moore's law. The paper argues that evolutionary search by means of genetic programming is a promising approach for achieving creative, human-competitive, automated design because illogic and creativity are inherent in the evolutionary process.

[1]  J. G. Ziegler,et al.  Optimum Settings for Automatic Controllers , 1942, Journal of Fluids Engineering.

[2]  D. Ross Computer-aided design , 1961, CACM.

[3]  John E. Ullmann Conversion and the import problem: a confluence of opportunities , 1970, IEEE Spectrum.

[4]  Don Lancaster Active-Filter Cookbook , 1975 .

[5]  H. S. Black,et al.  Inventing the negative feedback amplifier: Six years of persistent search helped the author conceive the idea “in a flash” aboard the old Lackawanna Ferry , 1977, IEEE Spectrum.

[6]  Stewart W. Wilson The Genetic Algorithm and Biological Development , 1987, ICGA.

[7]  Hiroaki Kitano,et al.  Designing Neural Networks Using Genetic Algorithms with Graph Generation System , 1990, Complex Syst..

[8]  John R. Koza,et al.  Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems , 1990 .

[9]  John R. Koza,et al.  Genetic generation of both the weights and architecture for a neural network , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[10]  Jaime E. Kardontchik,et al.  Introduction to the design of transconductor-capacitor filters , 1992, The Kluwer international series in engineering and computer science.

[11]  John R. Koza,et al.  Genetic Programming: The Movie , 1992 .

[12]  Frédéric Gruau,et al.  Genetic synthesis of Boolean neural networks with a cell rewriting developmental process , 1992, [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks.

[13]  John R. Koza,et al.  Genetic programming (videotape): the movie , 1992 .

[14]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[15]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[16]  Una-May O'Reilly,et al.  Genetic Programming II: Automatic Discovery of Reusable Programs. , 1994, Artificial Life.

[17]  Andre Vladimirescu,et al.  The Spice Book , 1994 .

[18]  John R. Koza,et al.  Genetic programming II (videotape): the next generation , 1994 .

[19]  O. Aaserud,et al.  Trends in current analog design - a panel debate , 1995 .

[20]  Karl Johan Åström,et al.  PID Controllers: Theory, Design, and Tuning , 1995 .

[21]  F. H. Bennett,et al.  Discovery by genetic programming of a cellular automata rule that is better than any known rule for the majority classification problem , 1996 .

[22]  Wolfgang Banzhaf,et al.  Genetic Programming: An Introduction , 1997 .

[23]  Thomas H. Lee,et al.  The Design of CMOS Radio-Frequency Integrated Circuits: RF CIRCUITS THROUGH THE AGES , 2003 .

[24]  Moshe Sipper,et al.  Evolvable Systems: From Biology to Hardware , 1998, Lecture Notes in Computer Science.

[25]  Astro Teller,et al.  Evolving Team Darwin United , 1998, RoboCup.

[26]  Lee Spector,et al.  Genetic programming for quantum computers , 1998 .

[27]  Sean Luke,et al.  Genetic Programming Produced Competitive Soccer Softbot Teams for RoboCup97 , 1998 .

[28]  Peter Nordin,et al.  Genetic programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications , 1998 .

[29]  John R. Koza Genetic Programming III - Darwinian Invention and Problem Solving , 1999, Evolutionary Computation.

[30]  N. Swamy,et al.  Finding a better-than-classical quantum AND/OR algorithm using genetic programming , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[31]  Lee Spector,et al.  Quantum computing applications of genetic programming , 1999 .

[32]  Adrian Stoica,et al.  Polymorphic Electronics , 2001, ICES.

[33]  Kurt Antreich,et al.  The sizing rules method for analog integrated circuit design , 2001, IEEE/ACM International Conference on Computer Aided Design. ICCAD 2001. IEEE/ACM Digest of Technical Papers (Cat. No.01CH37281).

[34]  Christofer Toumazou,et al.  The invention of CMOS amplifiers using genetic programming and current-flow analysis , 2002, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[35]  Matthew J. Streeter,et al.  Evolving inventions. , 2003, Scientific American.

[36]  John R. Koza,et al.  Genetic Programming IV: Routine Human-Competitive Machine Intelligence , 2003 .

[37]  V. Ramachandran,et al.  Hearing Colors, Tasting Shapes , 2003 .

[38]  Sina Balkir,et al.  Analog VLSI Design Automation , 2003 .

[39]  Gregory S. Hornby,et al.  An Evolved Antenna for Deployment on NASA's Space Technology 5 Mission , 2004 .

[40]  Hod Lipson,et al.  How to Draw a Straight Line Using a GP: Benchmarking Evolutionary Design Against 19 Century Kinematic Synthesis , 2004 .

[41]  John R. Koza,et al.  Discovery of Rewrite Rules in Lindenmayer Systems and State Transition Rules in Cellular Automata via Genetic Programming , 2004 .

[42]  Lee Spector,et al.  Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming) , 2004 .

[43]  Michel Toulouse,et al.  Automatic Quantum Computer Programming: A Genetic Programming Approach , 2006, Genetic Programming and Evolvable Machines.