From: tyadav@athena.mit.edu (Tapesh Yadav)
Subject: Re: Choice of optimization methods
Organization: Massachusetts Institute of Technology
Date: Wed, 22 Jul 1992 01:17:47 GMT

===============================
 JRBANGA@poppy.ucdavis.edu (JULIO RODRIGUEZ BANGA) writes:
>
> Though my work deals with optimization, I'm not an expert in the field, 
>so I'd like to receive opinions about the pros and cons of GA and direct 
>search methods when compared with 'politically correct' algorithms. 
>Particularly, if those methods are 'incorrect', why are they so widely used?.
>
>                               Julio.

 Genetic Algorithms (GAs). Well, few years ago, I spent thousands of hours 
 hacking code for it. All nighters and stuff like that. Ported it onto 
 Cray YMP in Columbus, Ohio. And played with it, just because it was fun. 
 Those days, I was heavy into systems, synthesis, optimization etc. etc. 
 So what follows is few words from someone who doesn't think himself as 
 an expert. But yes, I have met and talked with likes of Westerberg, 
 Stephanopoulos, Grossman... you get the picture.

 As you perhaps know, GAs attempt to simulate natural evolution. The 
 optimization problem is defined as an organism. The variables are translated
 into a chromosome. And then these chromosomes are mathematically reproduced.
 The reproduction involves mutation. Mutation creates new solutions. The
 better solution (survival of the fittest) reproduces more than others. And
 mathematically, the next generation keeps drifting towards better solutions.
 So, GAs are, conceptually, random number (stochastic) based. With tricks you
 can add rudimentary gradient search. But that has its own 
 troubles/challenges...

 The trouble with GAs are many:  
	- you don't know if you reached the global optimum. And you
	  don't know the quality of the solution you get.
	- you can easily get trapped in local optimum (dinosaur problem)
	- difficult to duplicate.
	- performance significantly depends on initial solution.
	- search is extremely inefficient. 
	- cpu time is excessive. 

 nevertheless, GAs have found some applications. See Genetic Algorithms
 by D Goldberg (sp).

 SQP, MINLP etc., in ChemE process synthesis work in particular, comes 
 from the Carnegie Mellon group. In particular, MINLP has been touted as
 the REAL work horse. It has its own problems. MINLP necessitates 
 restructuring the problem. Linear objective functions; may be quadratic
 at times. But thats it. Even after that the quality of solution in
 REALITY is unknown. Checking for Kuhn Tucker conditions is asking for
 too much. But still, these methods give results that can be duplicated,
 can be done faster, have no initial solution hassles. And these are
 methods are very convenient once you have played with them.

 Whether either methods give us better engineering, I sometimes wonder.

 As far as "politically correct" stuff goes, I think that is meaningless.
 IMHO, it is the problem that should determine which method is best, instead
 of fashion.

 Perhaps we should work with both methods. And synthesize them into one
 some day.

T.Y.
----
tyadav@athena.mit.edu
Xref: sparky sci.engr.chem:374 sci.chem:2711 sci.materials:541