This proposed text appears to be a good introduction to
evolutionary computation for use in applied statistics research.
The authors draw from a vast base of knowledge about the current
literature in both the design of evolutionary algorithms and
statistical techniques. Modern statistical research is on the
threshold of solving increasingly complex problems in high
dimensions, and the generalization of its methodology to parameters
whose estimators do not follow mathematically simple distributions
is underway. Many of these challenges involve optimizing functions
for which analytic solutions are infeasible. Evolutionary
algorithms represent a powerful and easily understood means of
approximating the optimum value in a variety of settings. The
proposed text seeks to guide readers through the crucial issues of
optimization problems in statistical settings and the
implementation of tailored methods (including both stand-alone
evolutionary algorithms and hybrid crosses of these procedures with
standard statistical algorithms like Metropolis-Hastings) in a
variety of applications. This book would serve as an excellent
reference work for statistical researchers at an advanced graduate
level or beyond, particularly those with a strong background in
computer science.
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