Hemanshu Kaul
Department of Mathematics
University of Illinois at Urbana-Champaign
Kauffman NK Model - A Stochastic Combinatorial
Optimization Model for Complex Systems
Abstract
Many scenarios in theoretical biology, physics, and management science
can be modeled as discrete complex systems with several interacting
components that can be in various states. The aim is to maximize
a performance measure involving contributions from each
component. This measure may depend on both the state of each
component and on interactions between components. In 1987, Kauffman and
Levin introduced a combinatorial optimization model for such systems,
called the Kauffman NK model, where N is the number of components of
the system and K measures the interaction between the components.
This was proposed to model the evolution of genomes in theoretical
biology but has since been applied in other areas as listed above.
Previous research on the NK model has emphasized simulations and
analysis of local optima. Here we focus on rigorous results for
global optima. We describe a computational setup using a stochastic
network model, which leads to applicable strategies for computing
bounds on global optima. Recent papers used tools from analysis and
probability to obtain bounds on the expected value of the global optima
for fixed K and large N. We present bounds when K grows with N, using
ideas from graph theory, probabilistic combinatorics and order
statistics. These general ideas are then applied to the analysis of the
cases when underlying distributions are uniform and normal
distributions.
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