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Abstract
Dynamic Optimization Problems (DOPs) have been widely studied using Evolutionary Algorithms (EAs). Yet, a clear and rigorous definition of DOPs is lacking in the Evolutionary Dynamic Optimization (EDO) community. In this paper, we propose a unified definition of DOPs based on the idea of multiple-decision-making discussed in the Reinforcement Learning (RL) community. We draw a connection between EDO and RL by arguing that both of them are studying DOPs according to our definition of DOPs. We point out that existing EDO or RL research has been mainly focused on some types of DOPs. A conceptualized benchmark problem, which is aimed at the systematic study of various DOPs, is then developed. Some interesting experimental studies on the benchmark reveal that EDO and RL methods are specialized in certain types of DOPs and more importantly new algorithms for DOPs can be developed by combining the strength of both EDO and RL methods.
Original language | English |
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Title of host publication | Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 1550-1557 |
Number of pages | 8 |
ISBN (Print) | 9781479914883 |
DOIs | |
Publication status | Published - 16 Sept 2014 |
Event | 2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing, China Duration: 6 Jul 2014 → 11 Jul 2014 |
Conference
Conference | 2014 IEEE Congress on Evolutionary Computation, CEC 2014 |
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Country/Territory | China |
City | Beijing |
Period | 6/07/14 → 11/07/14 |
ASJC Scopus subject areas
- Artificial Intelligence
- Computational Theory and Mathematics
- Theoretical Computer Science
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Dive into the research topics of 'What are dynamic optimization problems?'. Together they form a unique fingerprint.Projects
- 1 Finished
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Evolutionary Computation for Dynamic Optimisation in Network Environments
Engineering & Physical Science Research Council
25/02/13 → 17/08/17
Project: Research Councils