The Experts below are selected from a list of 18 Experts worldwide ranked by ideXlab platform

Yang Lou - One of the best experts on this subject based on the ideXlab platform.

  • sequential learnable evolutionary algorithm a research program
    Systems Man and Cybernetics, 2015
    Co-Authors: Shiu Yin Yuen, Xin Zhang, Yang Lou
    Abstract:

    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.

Shiu Yin Yuen - One of the best experts on this subject based on the ideXlab platform.

  • sequential learnable evolutionary algorithm a research program
    Systems Man and Cybernetics, 2015
    Co-Authors: Shiu Yin Yuen, Xin Zhang, Yang Lou
    Abstract:

    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.

Xin Zhang - One of the best experts on this subject based on the ideXlab platform.

  • sequential learnable evolutionary algorithm a research program
    Systems Man and Cybernetics, 2015
    Co-Authors: Shiu Yin Yuen, Xin Zhang, Yang Lou
    Abstract:

    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.

Sheldon H Jacobson - One of the best experts on this subject based on the ideXlab platform.

  • an application of the branch bound and remember algorithm to a new simple assembly line balancing dataset
    European Journal of Operational Research, 2014
    Co-Authors: David R Morrison, Edward C Sewell, Sheldon H Jacobson
    Abstract:

    The simple assembly line balancing Problem (SALBP) is a well-studied NP-complete Problem for which a new Problem Database of generated instances was published in 2013. This paper describes the application of a branch, bound, and remember (BB&R) algorithm using the cyclic best-first search strategy to this new Database to produce provably exact solutions for 86% of the unsolved Problems in this Database. A new backtracking rule to save memory is employed to allow the BB&R algorithm to solve many of the largest Problems in the Database.

David R Morrison - One of the best experts on this subject based on the ideXlab platform.

  • an application of the branch bound and remember algorithm to a new simple assembly line balancing dataset
    European Journal of Operational Research, 2014
    Co-Authors: David R Morrison, Edward C Sewell, Sheldon H Jacobson
    Abstract:

    The simple assembly line balancing Problem (SALBP) is a well-studied NP-complete Problem for which a new Problem Database of generated instances was published in 2013. This paper describes the application of a branch, bound, and remember (BB&R) algorithm using the cyclic best-first search strategy to this new Database to produce provably exact solutions for 86% of the unsolved Problems in this Database. A new backtracking rule to save memory is employed to allow the BB&R algorithm to solve many of the largest Problems in the Database.