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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Published on: December 9, 2012

Enabling the extended compact genetic algorithm for real-parameter optimization by using adaptive discretization.

Ying-ping Chen1, Chao-Hong Chen

  • 1Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan. ypchen@nclab.tw

Evolutionary Computation
|March 10, 2010
PubMed
Summary
This summary is machine-generated.

A novel split-on-demand (SoD) method enhances estimation of distribution algorithms (EDAs) for continuous optimization. This adaptive discretization approach improves performance, particularly when integrated with the extended compact genetic algorithm (ECGA).

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Area of Science:

  • Optimization Algorithms
  • Computational Intelligence
  • Machine Learning

Background:

  • Continuous optimization problems often require discretization for estimation of distribution algorithms (EDAs).
  • Existing discretization methods like fixed-height histogram (FHH) and fixed-width histogram (FWH) have limitations.
  • Adaptive discretization offers a potential improvement for EDAs in continuous domains.

Purpose of the Study:

  • To introduce and evaluate a new adaptive discretization method, split-on-demand (SoD), for EDAs.
  • To integrate SoD with the extended compact genetic algorithm (ECGA) and assess its performance.
  • To compare SoD-based ECGA against other discretization methods and existing approaches on benchmark and real-world problems.

Main Methods:

  • Developed the split-on-demand (SoD) adaptive discretization technique for continuous optimization.
  • Integrated SoD with the extended compact genetic algorithm (ECGA), forming a memetic algorithm framework.
  • Conducted numerical experiments using benchmark functions and the economic dispatch problem, comparing SoD-ECGA with FHH-ECGA, FWH-ECGA, and other established methods.

Main Results:

  • SoD demonstrated superior performance as a discretization method for ECGA compared to FHH and FWH.
  • The proposed ECGA with SoD framework achieved competitive or better results on benchmark functions.
  • ECGA with SoD significantly outperformed existing methods on the economic dispatch problem, yielding the best-known solutions.

Conclusions:

  • Split-on-demand (SoD) is an effective adaptive discretization method for enhancing EDAs in continuous optimization.
  • The integration of SoD with ECGA provides a robust memetic algorithm framework.
  • SoD-based ECGA offers a promising approach for solving complex real-world optimization problems like economic dispatch.