Paper Info
Title
Algorithm portfolio for individual-based surrogate-assisted evolutionary algorithms
Abstract
ABSTRACTSurrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation tools for computationally expensive problems (CEPs). However, a randomly selected algorithm may fail in solving unknown problems due to no free lunch theorems, and it will cause more computational resource if we re-run the algorithm or try other algorithms to get a much solution, which is more serious in CEPs. In this paper, we consider an algorithm portfolio for SAEAs to reduce the risk of choosing an inappropriate algorithm for CEPs. We propose two portfolio frameworks for very expensive problems in which the maximal number of fitness evaluations is only 5 times of the problem's dimension. One framework named Par-IBSAEA runs all algorithm candidates in parallel and a more sophisticated framework named UCB-IBSAEA employs the Upper Confidence Bound (UCB) policy from reinforcement learning to help select the most appropriate algorithm at each iteration. An effective reward definition is proposed for the UCB policy. We consider three state-of-the-art individual-based SAEAs on different problems and compare them to the portfolios built from their instances on several benchmark problems given limited computation budgets. Our experimental studies demonstrate that our proposed portfolio frameworks significantly outperform any single algorithm on the set of benchmark problems.
Year
DOI
Venue
2019
10.1145/3321707.3321715
Genetic and Evolutionary Computation Conference
Keywords
Field
DocType
Algorithm portfolio, surrogate model, evolutionary algorithm, computationally expensive problem
Mathematical optimization,Evolutionary algorithm,Computer science,No free lunch in search and optimization,Algorithm,Portfolio,Computational resource,Computation,Reinforcement learning
Journal
Volume
Citations 
PageRank 
abs/1904.09813
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Tong Hao1654.27
Jialin Liu212816.56
Yao Xin361838.64