Paper Info
Title
Generic Postprocessing Via Subset Selection For Hypervolume And Epsilon-Indicator
Abstract
Most biobjective evolutionary algorithms maintain a population of fixed size mu and return the final population at termination. During the optimization process many solutions are considered, but most are discarded. We present two generic postprocessing algorithms which utilize the archive of all non-dominated solutions evaluated during the search. We choose the best mu solutions from the archive such that the hypervolume or e-indicator is maximized. This postprocessing costs no additional fitness function evaluations and has negligible runtime compared to most EMOAs.We experimentally examine our postprocessing for four standard algorithms (NSGA-II, SPEA2, SMS-EMOA, IBEA) on ten standard test functions (DTLZ 1-2,7, ZDT 1-3, WFG 3-6) and measure the average quality improvement. The median decrease of the distance to the optimal e-indicator is 95%, the median decrease of the distance to the optimal hypervolume value is 86%. We observe similar performance on a real-world problem (wind turbine placement).
Year
DOI
Venue
2014
10.1007/978-3-319-10762-2_51
PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XIII
Keywords
Field
DocType
Wind Turbine,Pareto Front,Evolutionary Computation,Multiobjective Optimization,Subset Selection
Population,Mathematical optimization,Evolutionary algorithm,Computer science,Evolutionary computation,Multi-objective optimization,Fitness function
Conference
Volume
ISSN
Citations 
8672
0302-9743
12
PageRank 
References 
Authors
0.52
20
3
Name
Order
Citations
PageRank
Karl Bringmann142730.13
Tobias Friedrich245723.56
Patrick Klitzke3120.52