Multi-Objective Genetic Algorithm with Strategies for Dying of Solution

Rahila Patel, M. M. Raghuwanshi and Latesh Malik
Additional contact information
Rahila Patel: Research scholar, G. H. Raisoni College of Engineering, Nagpur, India
M. M. Raghuwanshi: Principal, Rajiv Gandhi College of Engineering and Research, Nagpur, India
Latesh Malik: Department of Computer Science & Engineering, G. H. Raisoni College of Engineering, Nagpur, India

International Journal of Applied Evolutionary Computation (IJAEC), 2014, vol. 5, issue 1, 69-85

Abstract: Genetic Algorithm (GA) mimics natural evolutionary process. Since dying of an organism is important part of natural evolutionary process, GA should have some mechanism for dying of solutions just like GA have crossover operator for birth of solutions. In nature, occurrence of event of dying of an organism has some reasons like aging, disease, malnutrition and so on. In this work we propose three strategies of dying or removal of solution from next generation population. Multi-objective Genetic Algorithm (MOGA) takes decision of removal of solution, based on one of these three strategies. Experiments were performed to show impact of dying of solutions and dying strategies on the performance of MOGA.

Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/ijaec.2014010105 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jaec00:v:5:y:2014:i:1:p:69-85

Access Statistics for this article

International Journal of Applied Evolutionary Computation (IJAEC) is currently edited by Sukhpal Singh Gill

More articles in International Journal of Applied Evolutionary Computation (IJAEC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().