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Gray-box optimization leverages the information available about the mathematical structure of an optimization problem in order to design efficient search operators. The DRILS algorithm (Deterministic Recombination with Iterated Local Search) has been used to solve Adjacent NK Landscape problems with up to one million variables to optimality. The deterministic recombination operator is Partition Crossover. In this paper, we use DRILS for bi-objective optimization where the target functions are Random and Adjacent NK Landscapes. Our approach introduces a new way of vectoring the target objective functions. This bi-objective implementation of DRILS is both extremely fast and highly effective when compared to general implementations of NSGA-II and MOEA/D, and it naturally extends to multi-objective implementations.

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Gray-Box Bi-objective Boolean Optimization Using Deterministic Recombination with Iterated Local Search

  • 会议论文

  • 发表日期：

    2026年

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  • Surendra Kurivella,
  • Darrell Whitley,
  • Francisco Chicano,
  • Gabriela Ochoa,
  • Francesco Cecere,
  • Bilel Derbel

摘要

Gray-box optimization leverages the information available about the mathematical structure of an optimization problem in order to design efficient search operators. The DRILS algorithm (Deterministic Recombination with Iterated Local Search) has been used to solve Adjacent NK Landscape problems with up to one million variables to optimality. The deterministic recombination operator is Partition Crossover. In this paper, we use DRILS for bi-objective optimization where the target functions are Random and Adjacent NK Landscapes. Our approach introduces a new way of vectoring the target objective functions. This bi-objective implementation of DRILS is both extremely fast and highly effective when compared to general implementations of NSGA-II and MOEA/D, and it naturally extends to multi-objective implementations.