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\title{Application of Simulated Annealing  and Genetic Algorithms to
Verified Global Optimization}
\author{Rajendra B. Patil}

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\markright{APIC'95, El Paso,
Extended Abstracts,
A Supplement to the international journal of {\rm Reliable
Computing}\ \ \ \ \ \ \ \ \ \ \  \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \
\ \ \ \ \ \ \ \ \ \ \ \ \ }

\maketitle

\begin{abstract}
Simulated annealing and genetic algorithms (GA's) are efficient but 
{\it heuristic} global
optimization techniques in the sense that they do not give a
guaranteed (verified) result. 

Interval global optimization 
methods do provide us with verified results, but they are
often very time-consuming. 
It is therefore desirable to combine efficiency of simulated annealing
and genetic algorithms with the verified character of interval
techniques. 

In this talk, we will describe such methods. The possibility to use
simulated annealing and GA is based on the fact that in the existing 
verified
global optimization methods, there is still room for choice: what box
to analyze first, how to divide the box into sub-boxes, etc. No matter
what choices we make, the algorithm still leads to a verified
optimization. However, different choices can make the algorithm much
faster or much slower. We use simulated annealing
and GA to make efficient choices. 
\end{abstract} 
  
\auffil{The author is with 
Computing Research and Applications (CIC-3),
MS -  M986,
Los Alamos National Laboratory,
Los Alamos, NM 87545,
email rbp@killdeer.lanl.gov.}

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