John Koza's Publications: Year Index:
Koza, John R., Bennett III, Forrest H, Andre, David, and
Keane, Martin A. 2001. Genetic Programming: Biologically Inspired
Computation that Creatively Solves Non-Trivial Problems. In
Landweber, Laura F. and Winfree, Erik (Editors). 2001. Evolution as
Computation, DIMACS Workshop,
This paper describes a biologically inspired domain-independent technique, called genetic programming, that automatically creates computer programs to solve problems. Starting with a primordial ooze of thousands of randomly created computer programs, genetic programming progressively breeds a population of computer programs over a series of generations using the Darwinian principle of natural selection, recombination (crossover), mutation, gene duplication, gene deletion, and certain mechanisms of developmental biology. The technique is illustrated by applying it to a non-trivial problem involving the automatic synthesis (design) of a lowpass filter circuit. The evolved results are competitive with human-produced solutions to the problem. In fact, four of the automatically created circuits exhibit human-level creativity and inventiveness, as evidenced by the fact that they correspond to four inventions that were patented between 1917 and 1936.
Click here for a PDF version of this 2001 book chapter consisting of our paper from the 1999 DIMACS conference.
Koza, John R., Mydlowec, William, Lanza, Guido, Yu, Jessen,
and Keane, Martin A. 2001a. Reverse engineering of
metabolic pathways from observed data using genetic programming. In Altman, Russ B. Dunker, A. Keith, Hunter, Lawrence, Lauderdale,
Kevin, and Klein, Teri (editors). Pacific Symposium
on Biocomputing 2001.
Recent work has demonstrated that genetic programming is capable of automatically creating complex networks (such as analog electrical circuits and controllers) whose behavior is modeled by linear and non-linear continuous-time differential equations and whose behavior matches prespecified output values. The concentrations of substances participating in networks of chemical reactions are also modeled by non-linear continuous-time differential equations. This paper demonstrates that it is possible to automatically create (reverse engineer) a network of chemical reactions from observed time-domain data. Genetic programming starts with observed time-domain concentrations of input substances and automatically creates both the topology of the network of chemical reactions and the rates of each reaction within the network such that the concentration of the final product of the automatically created network matches the observed time-domain data. Specifically, genetic programming automatically created metabolic pathways involved in the phospholipid cycle and the synthesis and degradation of ketone bodies.
Click here for a PDF version of this PSB-2001 conference paper on metabolic pathways.
Koza, John R., Mydlowec, William, Lanza, Guido, Yu,
Jessen, and Keane, Martin A. 2001b. Automatic synthesis of both the
topology and sizing of metabolic pathways using genetic programming. In Spector, Lee, Goodman, E., Wu, A., Langdon,
William B., Voigt, H.-M., Gen, M., Sen, S., Dorigo, Marco, Pezeshk, S., Garzon,
Max, and Burke, E. (editors). 2001. Proceedings
of the Genetic and Evolutionary Computation Conference, GECCO-2001.
The concentrations of substances participating in networks of chemical reactions are modeled by non-linear continuous-time differential equations. Recent work has demonstrated that genetic programming is capable of automatically creating complex networks (e.g., analog electrical circuits, controllers) whose behavior is modeled by linear and non-linear continuous-time differential equations and whose behavior matches prespecified output values. This paper describes how genetic programming can be used to automatically synthesize (reverse engineer) both the topology of the network of chemical reactions and the rates (sizing) of each reaction of a network such that the behavior of the automatically created network matches the observed time-domain data. Genetic programming has automatically created metabolic pathways that contain noteworthy topological features, such as an internal feedback loop, a bifurcation point where one substance is distributed to two different reactions, and an accumulation point where one substance is accumulated from two sources.
Click here for a PDF version of this GECCO-2001 conference paper
Koza, John R., Mydlowec, William, Lanza, Guido, Yu,
Jessen, and Keane, Martin A. 2001c. Automated reverse engineering of
metabolic pathways from Observed Data using genetic programming. In Kitano,
Hiroaki (editor). 2001. Foundations of Systems Biology.
Recent work has demonstrated that genetic programming is capable of automatically creating complex networks (e.g., analog electrical circuits, controllers) whose behavior is modeled by linear and non-linear continuous-time differential equations and whose behavior matches prespecified output values. The concentrations of substances participating in networks of chemical reactions are modeled by non-linear continuous-time differential equations. This chapter demonstrates that it is possible to automatically create (reverse engineer) a network of chemical reactions from observed time-domain data. Genetic programming starts with observed time-domain concentrations of substances and automatically creates both the topology of the network of chemical reactions and the rates of each reaction of a network such that the behavior of the automatically created network matches the observed time-domain data. Specifically, genetic programming automatically created a metabolic pathway involving four chemical reactions that consume glycerol and fatty acid as input, use ATP as a cofactor, and produce diacyl-glycerol as the final product. The metabolic pathway was created from 270 data points. The automatically created metabolic pathway contain three key topological features, including an internal feedback loop, a bifurcation point where one substance is distributed to two different reactions, and an accumulation point where one substance is accumulated from two sources. The topology and sizing of the entire metabolic pathway was automatically created using only the time-domain concentration values of diacyl-glycerol (the final product).
Click here for a PDF version of this chapter in 2001 Kitano book based on 2000 paper accepted (but not presented) at the First International Conference on Systems Biology (ICSB-2000) in Tokyo on November 14–16, 2000.
· The home page of Genetic Programming Inc. at www.genetic-programming.com.
· For information about the field of genetic programming and the field of genetic and evolutionary computation, visit www.genetic-programming.org
· The home page of John R. Koza at Genetic Programming Inc. (including online versions of most published papers) and the home page of John R. Koza at Stanford University
· For information about John Koza’s course on genetic algorithms and genetic programming at Stanford University
· Information about the 1992
book Genetic
Programming: On the Programming of Computers by Means of Natural Selection,
the 1994 book Genetic
Programming II: Automatic Discovery of Reusable Programs, the 1999
book Genetic
Programming III: Darwinian Invention and Problem Solving, and the
2003 book Genetic
Programming IV: Routine
Human-Competitive Machine Intelligence. Click here to read chapter 1 of Genetic
Programming IV book in PDF format.
· 3,440
published papers on genetic programming (as of November 28, 2003) in a
searchable bibliography (with many on-line versions of papers) by over 880
authors maintained by William Langdon’s and Steven M. Gustafson.
· For information on the Genetic Programming and Evolvable Machines journal published by Kluwer Academic Publishers
· For information on the Genetic Programming book series from Kluwer Academic Publishers, see the Call For Book Proposals
· For
information about the annual 2005
Genetic and Evolutionary Computation (GECCO) conference (which includes
the annual GP conference) to be held on June 25–29, 2005 (Saturday – Wednesday)
in
Last updated on August 23, 2004