IDEAS home Printed from https://ideas.repec.org/a/hin/jnljam/253714.html
   My bibliography  Save this article

Neurogenetic Algorithm for Solving Combinatorial Engineering Problems

Author

Listed:
  • M. Jalali Varnamkhasti
  • Nasruddin Hassan

Abstract

Diversity of the population in a genetic algorithm plays an important role in impeding premature convergence. This paper proposes an adaptive neurofuzzy inference system genetic algorithm based on sexual selection. In this technique, for choosing the female chromosome during sexual selection, a bilinear allocation lifetime approach is used to label the chromosomes based on their fitness value which will then be used to characterize the diversity of the population. The motivation of this algorithm is to maintain the population diversity throughout the search procedure. To promote diversity, the proposed algorithm combines the concept of gender and age of individuals and the fuzzy logic during the selection of parents. In order to appraise the performance of the techniques used in this study, one of the chemistry problems and some nonlinear functions available in literature is used.

Suggested Citation

  • M. Jalali Varnamkhasti & Nasruddin Hassan, 2012. "Neurogenetic Algorithm for Solving Combinatorial Engineering Problems," Journal of Applied Mathematics, Hindawi, vol. 2012, pages 1-12, September.
    • Handle: RePEc:hin:jnljam:253714
    DOI: 10.1155/2012/253714
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/JAM/2012/253714.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/JAM/2012/253714.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2012/253714?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Khaleed Alhazaymeh & Muhammad Gulistan & Majid Khan & Seifedine Kadry, 2019. "Neutrosophic Cubic Einstein Hybrid Geometric Aggregation Operators with Application in Prioritization Using Multiple Attribute Decision-Making Method," Mathematics, MDPI, vol. 7(4), pages 1-16, April.
    2. Derya Bakbak & Vakkas Uluçay & Memet Şahin, 2019. "Neutrosophic Soft Expert Multiset and Their Application to Multiple Criteria Decision Making," Mathematics, MDPI, vol. 7(1), pages 1-17, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnljam:253714. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.