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Evolution algorithm with adaptive genetic operator and dynamic scoring mechanism for large-scale sparse

Xia Wang1,2, Wei Zhao3,4, Jia-Ning Tang5,6

  • 1School of Electrical and Information Technology , Yunnan Minzu University, Kunming, 650504, China. wangxiacsu@163.com.

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Summary
This summary is machine-generated.

This study introduces SparseEA-AGDS, an advanced algorithm for large-scale sparse many-objective optimization. It enhances solution sparsity and convergence by dynamically adapting genetic operators and variable scores.

Keywords:
Adaptive geneticsDynamic scoringLarge-scale sparse evolutionary algorithmsMany-objectiveSparsity of Pareto solutions

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Area of Science:

  • Optimization algorithms
  • Computational intelligence
  • Machine learning

Background:

  • Large-scale sparse multi-objective optimization is crucial in fields like neural network training and pattern mining.
  • Existing algorithms struggle with sparsity requirements and search efficiency due to undifferentiated variable updates.
  • Previous methods like SparseEA had limitations with static variable scoring, hindering optimization.

Purpose of the Study:

  • To develop an improved algorithm, SparseEA-AGDS, for large-scale sparse many-objective optimization.
  • To enhance the generation of sparse Pareto optimal solutions and improve search efficiency.
  • To address the limitations of static scoring mechanisms in evolutionary optimization.

Main Methods:

  • Proposed SparseEA-AGDS, incorporating an adaptive genetic operator and dynamic scoring mechanism within the SparseEA framework.
  • Dynamically adjusted cross-mutation probabilities based on individual non-dominated layer levels.
  • Integrated a reference point-based environmental selection strategy to handle many-objective problems.
  • Updated decision variable scores dynamically to favor superior individuals.

Main Results:

  • SparseEA-AGDS demonstrated superior performance compared to five other algorithms on the SMOP benchmark dataset.
  • The algorithm achieved better convergence and diversity in many-objective optimization scenarios.
  • Generated high-quality sparse Pareto optimal solutions, meeting sparsity requirements effectively.

Conclusions:

  • SparseEA-AGDS significantly advances large-scale sparse many-objective optimization.
  • The adaptive genetic operator and dynamic scoring mechanism are key to improved performance.
  • The algorithm offers a robust solution for problems requiring sparse Pareto optimal outcomes.