- author = "John R. Koza",
- title = "Genetic evolution and co-evolution of computer programs",
- booktitle = "Artificial Life II",
- publisher = "Addison-Wesley",
- year = "1991",
- editor = "Christopher Taylor Charles Langton and J. Doyne Farmer and Steen Rasmussen",
- volume = "X",
- series = "SFI Studies in the Sciences of Complexity",
- address = "Santa Fe Institute, New Mexico, USA",
- publisher_address = "Redwood City, CA, USA",
- month = feb # " 1990",
- keywords = "genetic algorithms, genetic programming",
- pages = "603--629",
-
URL = "
http://www.genetic-programming.com/jkpdf/alife1990.pdf",
-
URL = "
https://www.amazon.co.uk/Artificial-Life-Proceedings-COMPLEXITY-PROCEEDINGS/dp/0201525704",
-
abstract = "Research in the field of artificial life focuses on
computer programs that exhibit some of the properties
of biological life (e.g. self-reproducibility,
evolutionary adaptation to an environment, etc.). In
one area of artificial life research, human programmers
write intentionally simple computer programs (often
incorporating observed features of actual biological
processes) and then study the 'emergent' higher level
behavior that may be exhibited by such seemingly simple
programs. In this chapter, we consider a different
problem, namely, 'How can computer programs be
automatically written by the computer itself using only
measurements of a given program's performance?' In
particular, this chapter describes the recently
developed 'genetic programming paradigm' which
genetically breeds populations of computer programs in
order to find a computer program that solves the given
problem. In the genetic programming paradigm, the
individuals in the population are hierarchical
compositions of functions and arguments. The
hierarchies are of various sizes and shapes.
Increasingly fit hierarchies are then evolved in
response to the problem environment using the genetic
operations of fitness proportionate reproduction
(Darwinian survival and reproduction of the fittest)
and crossover (sexual recombination). In the genetic
programming paradigm, the size and shape of the
hierarchical solution to the problem is not specified
in advance. Instead, the size and shape of the
hierarchy, as well as the contents of the hierarchy,
evolve in response to the Darwinian selective pressure
exerted by the problem environment.
This ALIFE-90 chapter also describes an extension of the genetic programming paradigm to the case where two (or more) populations of hierarchical computer programs simultaneously co-evolve. In co-evolution, each population acts as the environment for the other population. In particular, each individual of the first population is evaluated for 'relative fitness' by testing it against each individual in the second population, and, simultaneously, each individual in the second population is evaluated for relative fitness by testing them against each individual in the first population. Over a period of many generations, individuals with high 'absolute fitness' may evolve as the two populations mutually bootstrap each other to increasingly high levels of fitness.
The genetic programming paradigm is illustrated by genetically breeding a population of hierarchical computer programs to allow an 'artificial ant' to traverse an irregular trail. In addition, we genetically breed a computer program controlling the behavior of an individual ant in an ant colony which, when simultaneously executed by a large number of ants, causes the emergence of interesting collective behavior for the colony as a whole. Co-evolution is illustrated with a problem involving finding an optimal strategy for playing a simple discrete two-person competitive game represented by a game tree in extensive form.",
- notes = "This conference was held in 1990 but its proceedings were published in 1991.",
