Nonlinear regression analysis is an important process of statistics and poses many challenges to the user. While linear models are analytically solvable, nonlinear models can in most cases only be solved numerically. What many numeric methods have in common, is that they require a proper starting point to reach satisfactory results. A poor choice of starting values can greatly reduce the convergence speed or in many cases even result in the algorithm not to converge at all. This thesis proposes a genetic numerical hybrid method to approach the problem from a nontraditional angle. The approach combines genetic algorithms with traditional numeric methods and proposes a design suitable for massive parallelization with GPGPU computing. It is shown that the approach can solve a large set of practical test problems without having to specify any starting values and that is fast enough for practical use, utilizing only consumer grade hardware.

@mastersthesis{ANATNRABEP17,
	author	 = {Christopher Gerlach},
	title	 = {{A Numerical Approach to Nonlinear Regression Analysis by Evolving Parameters}},
	advisors	 = {Michael Kuhn},
	year	 = {2017},
	month	 = {06},
	school	 = {Universität Hamburg},
	howpublished	 = {{Online \url{https://wr.informatik.uni-hamburg.de/_media/research:theses:christopher_gerlach_a_numerical_approach_to_nonlinear_regression_analysis_by_evolving_parameters.pdf}}},
	type	 = {Master's Thesis},
	abstract	 = {Nonlinear regression analysis is an important process of statistics and poses many
			challenges to the user. While linear models are analytically solvable, nonlinear models can in
			most cases only be solved numerically. What many numeric methods have in common, is that they
			require a proper starting point to reach satisfactory results. A poor choice of starting
			values can greatly reduce the convergence speed or in many cases even result in the algorithm
			not to converge at all. This thesis proposes a genetic numerical hybrid method to approach the
			problem from a nontraditional angle. The approach combines genetic algorithms with traditional
			numeric methods and proposes a design suitable for massive parallelization with GPGPU
			computing. It is shown that the approach can solve a large set of practical test problems
			without having to specify any starting values and that is fast enough for practical use,
			utilizing only consumer grade hardware.},
}