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<title>Application Of Biodiversity Statistical Indexes To Control The Diversity Of Solutions In Multiobjective Genetic
Algorithms</title>
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<meta name="citation_title" content="Application Of Biodiversity Statistical Indexes To Control The Diversity Of Solutions In Multiobjective Genetic
Algorithms">
<meta name="citation_author" content="Jean Carlo de Elias Moreira">
<meta name="citation_author" content="Adinovam Henriques de Macedo Pimenta">
<meta name="citation_author_email" content="[email protected]">
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<meta name="citation_year" content="2020">
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content="Multiobjective Genetic Algorithm, Diversity, Pareto Frontier, Algoritmo Genético Multiobjetivo, Diversidade, Fronteira de Pareto">
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algorithms have been proposed, explored and successfully applied to a wide range of problems. Each of these
methodologies has distinct characteristics, typical advantages and characteristic disadvantages. However, there is a
problem that is shared by almost all of them: controlling the diversity of solutions. While natural selection favors
variations toward greater divergence, in artificial evolution candidate solutions are homogenized. The diversity of
solutions directly affects algorithmic performance, especially in multiobjective problems. Many authors have argued
that promoting diversity would be beneficial in evolutionary optimization processes and that this could help prevent
premature convergence into suboptimal solutions. In this paper we analyze some statistical indexes of biodiversity
widely used in ecology and their impact when inserted in evolutionary algorithms. We also suggest practical ways to
measure and promote diversity in multiobjective genetic algorithms.">
<meta property="article:author" content="Jean Carlo EM">
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<h1 class="fw-bolder">Application Of Biodiversity Statistical Indexes To Control The Diversity Of Solutions In
Multiobjective Genetic Algorithms</h1>
<p class="lead mb-0">Aplicação De Índices Estatísticos De Biodiversidade No Controle De Diversidade Das Soluções
Em Algoritmos Genéticos Multiobjetivo</p>
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<h2 class="card-title">Abstract</h2>
<p class="card-text">In the last decades, different optimization methodologies through evolutionary
algorithms have been proposed, explored and successfully applied to a wide range of problems. Each of
these methodologies has distinct characteristics, typical advantages and characteristic disadvantages.
However, there is a problem that is shared by almost all of them: controlling the diversity of solutions.
While natural selection favors variations toward greater divergence, in artificial evolution candidate
solutions are homogenized. The diversity of solutions directly affects algorithmic performance, especially
in multiobjective problems. Many authors have argued that promoting diversity would be beneficial in
evolutionary optimization processes and that this could help prevent premature convergence into suboptimal
solutions. In this paper we analyze some statistical indexes of biodiversity widely used in ecology and
their impact when inserted in evolutionary algorithms. We also suggest practical ways to measure and
promote diversity in multiobjective genetic algorithms.
</p>
<p><strong>Key-words</strong>: Multiobjective Genetic Algorithm. Diversity. Pareto Frontier.</p>
<h2 class="card-title">Resumo</h2>
<p class="card-text">Nas últimas décadas, diferentes metodologias de otimização por meio de algoritmos
evolutivos foram propostas, exploradas e aplicadas com sucesso a uma ampla gama de problemas. Cada uma
destas metodologias possui características distintas, vantagens típicas e desvantagens características. No
entanto, existe um problema que é compartilhado por quase todas elas: o controle da diversidade das
soluções. Enquanto a seleção natural favorece variações em direção a uma maior divergência, na evolução
artificial as soluções candidatas se homogeneízam. A diversidade das soluções afeta diretamente o
desempenho algorítmico, principalmente em problemas multiobjetivos. Muitos autores argumentaram que a
promoção da diversidade seria benéfica nos processos de otimização evolutiva e que isso poderia ajudar a
evitar a convergência prematura em soluções abaixo do ideal. Neste trabalho analisamos alguns índices
estatísticos de biodiversidade amplamente utilizados na ecologia e qual o seu impacto quando inseridos os
algoritmos evolutivos. Sugerimos, também, formas práticas de medir e promover a diversidade em algoritmos
genéticos multiobjetivo.</p>
<p><strong>Palavras-chaves</strong>: Algoritmo Genético Multiobjetivo. Diversidade. Fronteira de Pareto..
</p>
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<li><a target="_blank" href="https://nsgan.jcem.pro">Jean Carlo de Elias Moreira</a></li>
<li><a target="_blank" href="mailto:[email protected]">Adinovam Henriques de Macedo Pimenta</a></li>
</ul>
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<div class="card-header">Publishers</div>
<div class="card-body"><a target="_blank" href="https://fatece.edu.br">FATECE</a> in Scientific journal <a
target="_blank" href="https://fatece.edu.br/revista-perspectiva-volume-9">Perspectivas em Ciências
Tecnológicas, v. 9
(2020)</a>. ISSN: 2238-0507</div>
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<div class="card-header">Full Paper</div>
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<li><a target="_blank"
href="https://fatece.edu.br/arquivos/arquivos-revistas/perspectiva/volume9/Jean%20Carlo%20de%20Elias%20Moreira;%20Adinovam%20Henriques%20de%20Macedo%20Pimenta.pdf">Original
PDF</a></li>
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<li><a target="_blank" href="src/academic-article.pdf">Local PDF</a></li>
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<div class="card-header">Data Reposistory</div>
<div class="card-body">
<ul class="list-unstyled mb-0">
<li><a target="_blank" href="https://github.com/JeanCarloEM/nsgan">Github</a></li>
</ul>
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