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This study introduces a novel cross-representation genetic programming (GP) algorithm that evolves programs using both tree-based and linear representations simultaneously. This approach enhances GP

Keywords:
Cross-representationdynamic job shop scheduling.linear genetic programmingsymbolic regressiontree-based genetic programming

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Evolutionary Computation

Background:

  • Genetic programming (GP) methods often rely on specific representations (e.g., tree-based, linear), each with domain-dependent advantages and disadvantages.
  • The relationship between GP representations and fitness landscapes is complex, making it difficult to select the optimal representation for a given problem.
  • Simultaneously evolving programs with multiple representations allows exploration of diverse search spaces and potential synergistic benefits.

Purpose of the Study:

  • To address the gap in research on the simultaneous evolution of multiple GP representations.
  • To propose a novel cross-representation GP algorithm leveraging both tree-based and linear representations.
  • To investigate the effectiveness of inter-representation knowledge transfer in improving GP performance.

Main Methods:

  • Development of a cross-representation genetic programming (GP) algorithm.
  • Integration of both tree-based and linear representations within the GP framework.
  • Introduction of a novel cross-representation crossover operator designed to exploit the interplay between different representations.

Main Results:

  • Empirical evidence demonstrates that the proposed cross-representation GP approach enhances performance compared to single-representation GP.
  • Navigating learned knowledge between tree-based and linear representations improves effectiveness in symbolic regression tasks.
  • The algorithm shows improved effectiveness for dynamic job shop scheduling problems.

Conclusions:

  • Simultaneous evolution of multiple GP representations, specifically tree-based and linear, can lead to superior search capabilities.
  • The developed cross-representation crossover operator effectively harnesses the synergies between different representations.
  • This approach offers a promising direction for advancing GP effectiveness across various problem domains.