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Artificial Intelligence Algorithms Inspired By Life Sciences

Yıl 2018, Cilt: 5 Sayı: 3, 1233 - 1238, 01.09.2018
https://doi.org/10.18596/jotcsa.471300

Öz

Nature and life include many mysterious events, behaviors and format
within themselves. There is harmony between the environmental conditions,
behavior and forms of all living organism. Computer science, especially data
and information science, is based on the structure or behavior of living things
in the creation of many artificial intelligence algorithms by examining this
attitude of life. The rapid progress of the developing artificial intelligence
and information technology has increased the data and hidden data in our lives
many times and has tried to solve (1).



Artificial
intelligence has examined many areas or environments and has developed
approaches based on it. Expert systems, artificial neural networks, genetic
algorithms, inductive learning, explanation based learning, similarity based
learning, common sense information processing, database based reasoning, model
based reasoning, rational protection mechanism, distributed artificial
intelligence, natural language processing, chaos theory, logic programming are
the artificial intelligence algorithms used for these approaches (2). Among
artificial intelligence algorithms; The ant colony algorithm imitates the
behavior and direction of ants, and artificial neural networks imitates the
behavior and functions of neurons in the nervous system and genetic algorithms
imitates the theoretical form of genetic science (3, 4). Many algorithms such
as these algorithms are based on the vital form and behavior of living things. The
purpose of this review is the relations between the mentioned algorithms and
the living science are examined.

Kaynakça

  • 1. Fayyad U, Stolorz P. Data mining and KDD: Promise and challenges. Future generation computer systems. 1997;13(2-3):99–115.
  • 2. Baykal N, Beyan T. Bulanık mantık: uzman sistemler ve denetleyiciler. Bıçaklar Kitabevi; 2004.
  • 3. Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review. 1958;65(6):386.
  • 4. Holland JH. Genetic algorithms. Scientific american. 1992;267(1):66–73.
  • 5. Moschovakis YN. What is an algorithm? Içinde: Mathematics unlimited—2001 and beyond. Springer; 2001. s. 919–936.
  • 6. Turgut H. Veri madenciliği süreci kullanılarak alzheimer hastalığı teşhisine yönelik bir uygulama [Master Thesis]. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü; 2012
  • 7. Dorigo M, Maniezzo V, Colorni A. Dipartimento Di Elettronica-Politecnico Di Milano. 1991;
  • 8. Parpinelli RS, Lopes HS, Freitas AA. An ant colony algorithm for classification rule discovery. Içinde: Data mining: A heuristic approach. IGI Global; 2002. s. 191–208.
  • 9. Maniezzo V, Gambardella L, Luigi FD. New Optimization Techniques in Engineering. Içinde: An ANTS Heuristic for the Long-Term Car Pooling Problem. Springer Berlin, Heidelberg; 2004. s. 411–430.
  • 10. Gen M, Cheng R, Oren SS. Network design techniques using adapted genetic algorithms. Içinde: Evolutionary Design and Manufacture. Springer; 2000. s. 107–120.
  • 11. Beasley D, Bull DR, Martin RR. An overview of genetic algorithms: Part 1, fundamentals. University computing. 1993;15(2):56–69.
  • 12. Beasley D, Bull DR, Martin RR. A sequential niche technique for multimodal function optimization. Evolutionary computation. 1993;1(2):101–125.
  • 13. Wang S-C. Interdisciplinary computing in Java programming. C. 743. Springer Science & Business Media; 2012.
Yıl 2018, Cilt: 5 Sayı: 3, 1233 - 1238, 01.09.2018
https://doi.org/10.18596/jotcsa.471300

Öz

Kaynakça

  • 1. Fayyad U, Stolorz P. Data mining and KDD: Promise and challenges. Future generation computer systems. 1997;13(2-3):99–115.
  • 2. Baykal N, Beyan T. Bulanık mantık: uzman sistemler ve denetleyiciler. Bıçaklar Kitabevi; 2004.
  • 3. Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review. 1958;65(6):386.
  • 4. Holland JH. Genetic algorithms. Scientific american. 1992;267(1):66–73.
  • 5. Moschovakis YN. What is an algorithm? Içinde: Mathematics unlimited—2001 and beyond. Springer; 2001. s. 919–936.
  • 6. Turgut H. Veri madenciliği süreci kullanılarak alzheimer hastalığı teşhisine yönelik bir uygulama [Master Thesis]. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü; 2012
  • 7. Dorigo M, Maniezzo V, Colorni A. Dipartimento Di Elettronica-Politecnico Di Milano. 1991;
  • 8. Parpinelli RS, Lopes HS, Freitas AA. An ant colony algorithm for classification rule discovery. Içinde: Data mining: A heuristic approach. IGI Global; 2002. s. 191–208.
  • 9. Maniezzo V, Gambardella L, Luigi FD. New Optimization Techniques in Engineering. Içinde: An ANTS Heuristic for the Long-Term Car Pooling Problem. Springer Berlin, Heidelberg; 2004. s. 411–430.
  • 10. Gen M, Cheng R, Oren SS. Network design techniques using adapted genetic algorithms. Içinde: Evolutionary Design and Manufacture. Springer; 2000. s. 107–120.
  • 11. Beasley D, Bull DR, Martin RR. An overview of genetic algorithms: Part 1, fundamentals. University computing. 1993;15(2):56–69.
  • 12. Beasley D, Bull DR, Martin RR. A sequential niche technique for multimodal function optimization. Evolutionary computation. 1993;1(2):101–125.
  • 13. Wang S-C. Interdisciplinary computing in Java programming. C. 743. Springer Science & Business Media; 2012.
Toplam 13 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm DERLEME MAKALELER
Yazarlar

Hüseyin Turgut 0000-0001-8877-3179

Yayımlanma Tarihi 1 Eylül 2018
Gönderilme Tarihi 16 Ekim 2018
Kabul Tarihi 23 Ekim 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 5 Sayı: 3

Kaynak Göster

Vancouver Turgut H. Artificial Intelligence Algorithms Inspired By Life Sciences. JOTCSA. 2018;5(3):1233-8.