Paper Info
Title
Bio-Inspired Algorithms And Preferences For Multi-Objective Problems
Abstract
Multi-objective optimization evolutionary algorithms have been applied to solve many real-life decision problems. Most of them require the management of trade-offs between multiple objectives. Reference point approaches highlight a preferred set of solutions in relevant areas of Pareto frontier and support the decision makers to take more confidence evaluation. This paper extends some well-known algorithms to work with collective preferences and interactive techniques. In order to analyse the results driven by the online reference points, two new performance indicators are introduced and tested against some synthetic problem.
Year
DOI
Venue
2016
10.1007/978-3-319-32034-2_20
HYBRID ARTIFICIAL INTELLIGENT SYSTEMS
Keywords
Field
DocType
Collective intelligence, Preferences, Reference points, Evolutionary multi-bjective optimization algorithms
Performance indicator,Decision problem,Evolutionary algorithm,Computer science,Collective intelligence,Algorithm,Artificial intelligence,Pareto principle,Machine learning
Conference
Volume
ISSN
Citations 
9648
0302-9743
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Daniel Cinalli100.34
Luis Martí2439.51
Nayat Sanchez-Pi3262.92
Ana Cristina Bicharra Garcia424750.45