Evolutionary Optimization of Neural Networks For DNA Sequencing
Jim Golden, Director of Bioinformatics,
GeneSys Technologies
2:30 pm Fri Dec 6 2310 CS & Stats
There are many methods available to the engineer for modeling a system
such as an automated DNA sequencing instrument. Many of these use an
inductive approach to modeling, including numerical approximations from
ordinary-derivative models, numerical solutions from partial-derivative
models, and numerical simulations using finite elements. Because these
estimation procedures are inductive, they requirethe engineer to write
down a mathematical model of how function outputs depend on inputs. The
initial guess at the shape of a function based on a desired behavior
tightly constrains the categories of functions that can be estimated.
Neural networks approximate functions using raw sample data; estimation
is deductive and allows the engineer to explore a wider range of
possible functions governing behavior without having to know what the
functions are. Neural networks, however, suffer from two shortcomings:
it is difficult to specify an effective network architecture given the
specifications of a problem, and it is very easy for gradient algorithms
to get stuck in local minima when learning network weights. Genetic
algorithms are a robust method of near optimal search on functions. For
ill-defined problems, such as finding the best neural network for
modeling a set of test data, genetic algorithms offer a novel and robust
method for searching the space of possible designs.