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(Referência obtida automaticamente do SciELO, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

GENETIC ALGORITHM FOR OPTIMIZATION OF THE AEDES AEGYPTI CONTROL STRATEGIES

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Autor(es):
Helenice O. Florentino [1] ; Daniela R. Cantane [2] ; Fernando L.P. Santos [3] ; Célia A. Reis [4] ; Margarida V. Pato [5] ; Dylan Jones ; Marianna Cerasuolo [7] ; Rogério A. Oliveira [8] ; Luiz G. Lyra [9]
Número total de Autores: 9
Afiliação do(s) autor(es):
[1] UNESP. Departamento de Bioestatística - IB - Brasil
[2] UNESP. Departamento de Bioestatística - IB - Brasil
[3] UNESP. Departamento de Bioestatística - IB - Brasil
[4] UNESP. Departamento de Matemática - FC - Brasil
[5] Universidade de Lisboa. CMAFCIO. ISEG - Portugal
[7] University of Portsmouth. Department of Mathematics - Reino Unido
[8] UNESP. Departamento de Bioestatística - IB - Brasil
[9] UNESP. Departamento de Bioestatística - IB - Brasil
Número total de Afiliações: 9
Tipo de documento: Artigo Científico
Fonte: Pesquisa Operacional; v. 38, n. 3, p. 389-411, 2018-12-00.
Resumo

ABSTRACT Dengue Fever, Zika and Chikungunya are febrile infectious diseases transmitted by the Aedes species of mosquito with a high rate of mortality. The most common vector is Aedes aegypti. According to World Health Organization outbreaks of mosquito-borne illnesses are common in the tropical and subtropical climates, as there are currently no vaccines to protect against Dengue Fever, Chikungunya or Zika diseases. Hence, mosquito control is the only known method to protect human populations. Consequently, the affected countries need urgently search for better tools and sustained control interventions in order to stop the growing spread of the vector. This study presents an optimization model, involving chemical, biological and physical control decisions that can be applied to fight against the Aedes mosquito. To determine solutions for the optimization problem a genetic heuristic is proposed. Through the computational experiments, the algorithm shows considerable efficiency in achieving solutions that can support decision makers in controlling the mosquito population. (AU)