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Quad Search and Hybrid Genetic Algorithms
Darrell Whitley, Deon Garrett, and Jean-Paul Watson
Department of Computer Science
Colorado State University
Fort Collins, Colorado 80523, USA
{whitley,garrett,watsonj}@cs.colostate.edu
Abstract.
A bit climber using a Gray encoding is guaranteed to converge to a
global optimum in fewer than 
evaluations on unimodal 1-D
functions and on multi-dimensional sphere functions, where 
bits are used to encode the function domain. Exploiting these
ideas, we have constructed an algorithm we call Quad Search.
Quad Search converges to a local optimum on unimodal 1-D functions
in not more than 
function evaluations. For unimodal 1-D
and separable multi-dimensional functions, the result is the
global optimum. We empirically assess the performance of steepest
ascent local search, next ascent local search, and Quad Search.
These algorithms are also compared with Evolutionary Strategies.
Because of its rapid convergence time, we also use Quad Search to
construct a hybrid genetic algorithm. The resulting algorithm is
more effective than hybrid genetic algorithms using steepest
ascent local search or the RBC next ascent local search algorithm.
LNCS 2724, p. 1469 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003