NIPS*90 workshop at Keystone, CO. 11/30/90 or 12/1/90

[David H Ackley]

		 Genetic Algorithms, Neural Networks,
			 and Artificial Life

			   David H. Ackley
			   Richard K. Belew

Based on the principles of natural selection, "genetic algorithms"
(GAs) are a class of adaptive techniques that use a population of
structures to represent a set of potential solutions to some problem.
Selective reproduction emphasizes "more fit" individuals and focuses
the search process, while genetic operators modify the offspring to
increase diversity and search broadly.  Theoretical and empirical
results highlight the importance of employing the "crossover" operator
to exchange information between individuals.  Such genetic
recombination produces a global search strategy quite different from
--- and in some ways complementary to --- the gradient-based
techniques popular in neural network learning.

We will survey the theory and practice of genetic algorithms, and then
focus on the growing body of research efforts that combine genetic
algorithms and neural networks.  Brief presentations from researchers
active in the field (including Richard Lippmann, David Stork, and
Darrell Whitley) will set the stage for in-depth discussions of issues
in the area, such as:
  * Comparison and composition of GA sampling and NNet searching
  * The advantages and costs of recombination operators
  * Parallel implementations of GAs
  * Appropriate representations for NNets with the GA
  * Roles for ontogeny between GA evolution and NNet learning 

During the course of the workshop we will gradually broaden our scope.
As natural neurons provide inspiration for artificial neural networks,
and natural selection provides inspiration for GAs, other aspects of
natural life can provide inspirations for studies in "artificial
life".  We will sample recent "alife" research efforts, and conclude
with a discussion of prospects and problems for this new,
interdisciplinary field.