Paper Info
Title
Indicator-Based Selection in Multiobjective Search
Abstract
This paper discusses how preference information of the decision maker can in general be integrated into multiobjective search. The main idea is to first define the optimization goal in terms of a binary performance measure (indicator) and then to directly use this measure in the selection process. To this end, we propose a general indicator-based evolutionary algorithm (IBEA) that can be combined with arbitrary indicators. In contrast to existing algorithms, IBEA can be adapted to the preferences of the user and moreover does not require any additional diversity preservation mechanism such as fitness sharing to be used. It is shown on several continuous and discrete benchmark problems that IBEA can substantially improve on the results generated by two popular algorithms, namely NSCA-II and SPEA2, with respect to different performance measures.
Year
DOI
Venue
2004
10.1007/978-3-540-30217-9_84
Lecture Notes in Computer Science
Keywords
Field
DocType
evolutionary algorithm
Process selection,Mathematical optimization,Evolutionary algorithm,Computer science,Fitness sharing,Multiobjective programming,Artificial intelligence,Knapsack problem,Genetic algorithm,Machine learning,Decision maker,Binary number
Conference
Volume
ISSN
Citations 
3242
0302-9743
754
PageRank 
References 
Authors
23.47
12
2
Search Limit
100754
Name
Order
Citations
PageRank
Eckart Zitzler14678291.01
Simon Künzli2105940.86