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New style for PEO

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git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@789 331e1502-861f-0410-8da2-ba01fb791d7f
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canape 2007-11-16 11:34:20 +00:00
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trunk/paradiseo-peo/src/peo.h
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@ -1,4 +1,4 @@
/*
/*
* <peo.h>
* Copyright (C) DOLPHIN Project-Team, INRIA Futurs, 2006-2007
* (C) OPAC Team, LIFL, 2002-2007
@ -114,7 +114,7 @@
//! for nonlinear numerical optimization). Whereas some time-out detection can be used to
//! address the former issue, the latter one can be partially overcome if the grain is set to very
//! small values, as individuals will be sent out for evaluations upon request of the workers.</li>
//!
//!
//! <li> <i>Distributed evaluation of a single solution</i>. The quality of each solution is evaluated in
//! a parallel centralized way. That model is particularly interesting when the evaluation
//! function can be itself parallelized as it is CPU time-consuming and/or IO intensive. In
@ -146,11 +146,11 @@
//! <ul>
//! <li><i>Parallel exploration of neighboring candidates</i>. It is a low-level Farmer-Worker model
//! that does not alter the behavior of the heuristic. A sequential search computes the same
//! results slower.At the beginning of each iteration, the farmer duplicates the current solution
//! results slower.At the beginning of each iteration, the farmer duplicates the current solution
//! between distributed nodes. Each one manages some candidates and the results are returned to the farmer.
//! The model is efficient if the evaluation of a each solution is time-consuming and/or there are a great
//! deal of candidate neighbors to evaluate. This is obviously not applicable to SA since only one candidate
//! is evaluated at each iteration. Likewise, the efficiency of the model for HC is not always guaranteed as
//! The model is efficient if the evaluation of a each solution is time-consuming and/or there are a great
//! deal of candidate neighbors to evaluate. This is obviously not applicable to SA since only one candidate
//! is evaluated at each iteration. Likewise, the efficiency of the model for HC is not always guaranteed as
//! the number of neighboring solutions to process before finding one that improves the current objective function may
//! be highly variable.</li>
//!
@ -278,7 +278,7 @@
//!
//! The basisc of the ParadisEO framework philosophy are exposed in a few simple tutorials:
//! <ul>
//! <li>
//! <li>
//! <a href="lesson1/html/main.html" style="text-decoration:none;"> creating a simple ParadisEO evolutionary algorithm</a>;
//! </li>
//! </ul>