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From: androula@cb.ecn.purdue.edu (Ioannis Androulakis)
Newsgroups: comp.ai.neural-nets
Subject: simulated annealing vs genetic algs.
Keywords: simulated annealing, genetic algorithms
Message-ID: <1990Oct31.172833.18197@ecn.purdue.edu>
Date: 31 Oct 90 17:28:33 GMT
Sender: news@ecn.purdue.edu (USENET news)
Organization: Purdue University Engineering Computer Network
Lines: 34


   I have the following questions :

   1) How would you compare genetic algorithms with
   simulated annealing ? For some people, it appears
   that due to the lack of a sound theoretical background,
   that could quarantee asymptotic convergence, GA's
   have nothig new to say. Further, GAs, should be
   classified as a "heuristic" rather than just 
   a "stochastic" search technique.
   Do you happen to have any comments on these or any
   other issue concerning the comparison between GA's and
   SA ?

   2) A lot of people are using simulated annealing techniques
   to search continuous domains. From what I know, the
   proof of asymptotic convergence of SA, under suitable
   annealing schedules etc., was established under the assumption
   that the state transitions where performed in a discrete
   domain. If this is so, how legitimate is it to use a convergence
   result, derived for discrete state transitions, to problems
   where the state transitions are performed in a continous
   space ? Or, in other words, how can we prove convergence of
   continuous SA ?

   3) Are you aware of any applications of simulated annealing
   to constrained non-linear optimization ?
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   Thank you for your attention,

   ioannis

   androula@lips.ecn.purdue.edu