Paper Info
Title
Ordered Incremental Multi-Objective Problem Solving Based on Genetic Algorithms
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.
Year
DOI
Venue
2010
10.4018/jaec.2010040101
IJAEC
Keywords
Field
DocType
incremental nature,incremental algorithm,state-of-the-art mogas,ordered incremental multi-objective problem,various mogas,genetic algorithm-ii,incremental multiple objective genetic,incremental approach,potential best performance,multiple objective genetic algorithms,mogas,genetic algorithm
Mathematical optimization,Evolutionary algorithm,Computer science,Spea,Sorting,Evolution strategy,Artificial intelligence,Genetic algorithm,Pareto principle,Machine learning
Journal
Volume
Issue
Citations 
1
2
3
PageRank 
References 
Authors
0.42
17
3
Name
Order
Citations
PageRank
Wenting Mo1302.28
Sadasivan Puthusserypady218127.49
Sheng-Uei Guan345855.76