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nsgaII.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<title>jMetal Web site</title>
<link rel="stylesheet" href="css/style.css" />
</head>
<body>
<header id="header">
<h1 id="logo">
<a href="index.html" title="index"><img src="images/jMetalLogo.png" alt="jMetal Logo" /></a>
</h1>
<p>A Framework for Multi-Objective Optimization</p>
</header>
<nav>
<ul>
<li><a href="index.html">Home</a></li>
<li><a href="algorithms.html">Algorithms</a>
</li>
<li><a href="problems.html">Problems</a>
<li><a href="resources.html">Resources</a></li>
<li><a href="smpso.html" id="active">Our Techniques <span>▼</span></a>
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<li><a href="mocell.html">MOCell</a></li>
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<li><a href="nsgaII.html">NSGA-II variants</a></li>
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<article class="columms_container">
<header id="articleHeader">
<h1><strong>NSGA-II</strong> variants</h1>
</header>
<!--<hr>-->
<aside>
<header>
</header>
<section>
<h2 class=invert>NSGA-IIr Pseudo-code</h2>
<br />
<img src="resources/NSGAIIrCode.png" alt="nsgaIIr pseudocode" class="asideImage expand">
<h2 class=invert>Examples of Pareto front approximations</h2>
<br />
<img src="resources/ExamplesFronts.png" alt="fronts" class="asideImage expand">
</section>
</aside>
<section class="col2_3">
<header><h1>Description</h1></header>
<p>
<strong>NSGA-II</strong> (Non-dominated Sorting Genetic Algorithm II) if by far the most well known and most used multi-objective optimization metaheuristic.
It was the first algorithm implemented in jMetal and currently the framework provides a number of versions.
</p>
<header>
<h1>Variants</h1>
</header>
<ul>
<li>
<strong>ssNSGAII</strong> (steady-state NSGA-II). NSGA-II is a generational genetic algorithm and ssNSGA-II is the steady-state version of it.
</li>
<li>
<strong>pNSGAII</strong> (parallel NSGA-II). This version can take advantage of the multicores of current processors to perform the function evaluations of different individuals in parallel.
</li>
<li>
<strong>NSGAIIr</strong> (random NSGA-II). NSGA-IIr is basically NSGA-II with three variation operators
(SBX crossover, polynomial mutation and differential evolution) which are selected randomly to create new individuals.
</li>
<li>
<strong>NSGAIIa</strong> (adaptive NSGA-II). This algorithm works as NSGA-IIr, but the operators are selected adaptively.
</li>
</ul>
<header>
<h1>References</h1>
</header>
<ul>
<li>
A.J. Nebro, J.J. Durillo, On the Effect of Applying a Steady-State Selection Scheme in the Multi-Objective Genetic Algorithm NSGA-II. In R. Chion (Ed.): Nature-Inspired Algorithms for Optimization, pp. 435 - 456, Springer 2009.
<a href="http://dx.doi.org/10.1007/978-3-642-00267-0_16">DOI</a>
</li>
<li>
Antonio J. Nebro,
Juan J. Durillo,
Mirialys Machín,
Carlos A. Coello Coello,
Bernabé Dorronsoro, A Study of the Combination of Variation Operators in the NSGA-II Algorithm. Proceedings of the 15th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2013, Madrid, Spain, September 17-20, 2013.
Lecture Notes in Computer Science Volume 8109, 2013, pp 269-278.
<a href="http://dx.doi.org/10.1007/978-3-642-40643-0_28">DOI</a>
</li>
</ul>
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