🚀 go-pugleaf

In article <1993Jan19.155159.16736@thunder.mcrcim.mcgill.edu>  
gblais@McRCIM.McGill.EDU (Gerard Blais) writes:
> 
> 
> I have been using a genetic algorithm to solve a minimization
> problem.  I performed the same minimization using the "Very Fast
> Simulated Reannealing" algorithm created by Lester Ingber and
> Bruce Rosen (I got it from a friend, but I believe it's available
> from the net).  In all cases I tried, the Annealing search was
> orders of magnitude faster for converging to the global minimum
> than the GA.  My friend observed the same result for a different
> minimization problem.
> 
> My question is:  Are there any cases that you know of where a
> genetic algorithm would actually perform better than simulated
> annealing?  Are there any cases where simulated annealing would
> fail but not genetic algorithms?

One can view simulated annealing and reannealing as a GA with a mutation  
rate set to non-monotonic decreasing and the crossover and inversion rates  
set to zero.  From the few cases that I've read about the successes of GA,  
crossover is particularly good for a problem that has a natural  
hierarchical structure to the optimal solution, since crossover exploits  
local fitness maxima in the construction of a global maximum.  If there is  
no hierarchical structure to the optimal solution, local fitness  
contributes very little to global fitness, and there is little advantage  
to keeping local good solutions as crossover does.  Inversion exploits  
symmetries in the problem. 
Some problems that may be naturally hierarchical and symmetric, such as  
image classification, may yield to GA methods more than simulated  
annealing or simulated reannealing.
--
Richard Long
Institute for Simulation and Training
University of Central Florida
12424 Research Parkway, Suite 300, Orlando, FL 32826
(407)658-5026, FAX: (407)658-5059
long@acme.ucf.edu