Efficient Genetic Algorithms using Discretization Scheduling

In many applications of genetic algorithms, there is a
tradeoff between speed and accuracy in fitness evaluations when
evaluations are relaxed from using numerical methods such as
numerical integration. In these types of applications, the cost
and accuracy vary from discretization errors when implicit or
explicit quadrature is used to estimate the function evaluations.
There may be several functions with different grid sizing to
obtain a given solution quality. This thesis examines
discretization scheduling, or how to vary the
discretization within the genetic algorithm in order to use the
least amount of computation time for a solution of a desired
quality. The effectiveness of discretization scheduling can be
determined by comparing its computation time to the computation
time of a GA using a constant discretization. Time budgeting is
used to estimate the computational resources needed, and there are
three ingredients for the discretization scheduling: population
sizing, estimated time for each function evaluation and predicted
convergence time analysis. Idealized one- and two-dimensional
experiments and an inverse groundwater application illustrate the
computational savings to be achieved from using discretization

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