Sequential Learnable Evolutionary Algorithm: A Research Program

Shiu Yin YUEN, Xin ZHANG, Yang LOU

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

5 Citations (Scopus)


Evolutionary algorithms are typically run several times in design optimization problems and the best solution taken. We propose a novel online algorithm selection framework that learns to use the best algorithm based on previous runs, hence in effect using different and better algorithms as the search progresses. First, a set of algorithms are run on a benchmark problem suite. Given a new problem, a default algorithm is run and its convergence characteristics are recorded. This is used to map to the problem database to find the most similar problem. In turn, the database returns the best algorithm for this problem and this algorithm is run in the second iteration and so on, aiming to home onto the most suitable algorithm for the problem. The resulting algorithm, named Sequential Learnable Evolutionary algorithm (SLEA), outperforms Covariance Matrix Adaptation Evolution Strategy (CMA-ES) with multi-restarts. SLEA is also applied to a new problem, a real world application, and learns its characteristics. Experimental results show that it can correctly select the best algorithm for the problem. Finally, this paper proposes a new research program which learns the algorithm-problem mapping through solving real world problems accessed through the web and worldwide cooperation through Wikipedia.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Systems, Man, and Cybernetics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781479986965
Publication statusPublished - Oct 2015
Externally publishedYes
EventIEEE International Conference on Systems, Man, and Cybernetics, SMC 2015 - Kowloon Tong, Hong Kong
Duration: 9 Oct 201512 Oct 2015


ConferenceIEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
Country/TerritoryHong Kong
CityKowloon Tong

Bibliographical note

Publisher Copyright:
© 2015 IEEE.


  • algorithm selection
  • design optimization problems
  • multi-restart algorithm
  • new research program


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