Scholarlink

<p>This paper proposes a adaptive search-space scaling (AdSS) mechanism for genetic algorithms and studies its influence on mixed continuous–discrete optimisation. The method combines binary encoding, clustering in the continuous subspace, and an Individual Cluster Efficiency (ICE) based procedure for adaptive modification of the search domain, complemented by elitism and immigration. The same mechanism is integrated into a self-configuring genetic algorithm, forming the SelfCGA+AdSS variant, while preserving the internal self-adaptation of evolutionary operators. The algorithms are evaluated on the CEC&#xa0;2017 benchmark suite for multiple dimensionalities. The results demonstrate that AdSS consistently improves solution quality and reduces performance variability across runs, particularly on multimodal and composition functions. A detailed analysis of the composition function <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(f_{21}\)</EquationSource> </InlineEquation> at <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(D=10\)</EquationSource> </InlineEquation>, supported by t-SNE population visualisations, reveals oscillatory convergence behaviour caused by interactions between adaptive domain contraction, cluster filtering, and diversity loss. The study confirms the effectiveness of dynamic search space scaling and identifies key factors influencing its stability.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Adaptive search space scaling in self-adaptive genetic algorithms for high-dimensional global black-box optimization problems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月7日

查看全文
  • Ivan P. Malashin,
  • Evgenii Sopov,
  • Vladimir Nelyub,
  • Aleksei Borodulin,
  • Andrei Gantimurov,
  • Vadim Tynchenko

摘要

This paper proposes a adaptive search-space scaling (AdSS) mechanism for genetic algorithms and studies its influence on mixed continuous–discrete optimisation. The method combines binary encoding, clustering in the continuous subspace, and an Individual Cluster Efficiency (ICE) based procedure for adaptive modification of the search domain, complemented by elitism and immigration. The same mechanism is integrated into a self-configuring genetic algorithm, forming the SelfCGA+AdSS variant, while preserving the internal self-adaptation of evolutionary operators. The algorithms are evaluated on the CEC 2017 benchmark suite for multiple dimensionalities. The results demonstrate that AdSS consistently improves solution quality and reduces performance variability across runs, particularly on multimodal and composition functions. A detailed analysis of the composition function \(f_{21}\) at \(D=10\) , supported by t-SNE population visualisations, reveals oscillatory convergence behaviour caused by interactions between adaptive domain contraction, cluster filtering, and diversity loss. The study confirms the effectiveness of dynamic search space scaling and identifies key factors influencing its stability.