Welcome to the InfoSci Platform
Reference Hub2
Ordered Incremental Multi-Objective Problem Solving Based on Genetic Algorithms

Ordered Incremental Multi-Objective Problem Solving Based on Genetic Algorithms

Wenting Mo, Sheng-Uei Guan, Sadasivan Puthusserypady
Copyright: © 2010 |Volume: 1 |Issue: 2 |Pages: 27
ISSN: 1942-3594|EISSN: 1942-3608|EISBN13: 9781609604226|DOI: 10.4018/jaec.2010040101
Cite Article Cite Article

MLA

Mo, Wenting, et al. "Ordered Incremental Multi-Objective Problem Solving Based on Genetic Algorithms." IJAEC vol.1, no.2 2010: pp.1-27. http://doi.org/10.4018/jaec.2010040101

APA

Mo, W., Guan, S., & Puthusserypady, S. (2010). Ordered Incremental Multi-Objective Problem Solving Based on Genetic Algorithms. International Journal of Applied Evolutionary Computation (IJAEC), 1(2), 1-27. http://doi.org/10.4018/jaec.2010040101

Chicago

Mo, Wenting, Sheng-Uei Guan, and Sadasivan Puthusserypady. "Ordered Incremental Multi-Objective Problem Solving Based on Genetic Algorithms," International Journal of Applied Evolutionary Computation (IJAEC) 1, no.2: 1-27. http://doi.org/10.4018/jaec.2010040101

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Many Multiple Objective Genetic Algorithms (MOGAs) have been designed to solve problems with multiple conflicting objectives. Incremental approach can be used to enhance the performance of various MOGAs, which was developed to evolve each objective incrementally. For example, by applying the incremental approach to normal MOGA, the obtained Incremental Multiple Objective Genetic Algorithm (IMOGA) outperforms state-of-the-art MOGAs, including Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA) and Pareto Archived Evolution Strategy (PAES). However, there is still an open question: how to decide the order of the objectives handled by incremental algorithms? Due to their incremental nature, it is found that the ordering of objectives would influence the performance of these algorithms. In this paper, the ordering issue is investigated based on IMOGA, resulting in a novel objective ordering approach. The experimental results on benchmark problems showed that the proposed approach can help IMOGA reach its potential best performance.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.