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Genetic programming's evolution is shaped by downstream learners

By PulseAugur Editorial · [1 sources] ·

Researchers have explored how downstream learners influence the evolutionary process in genetic programming. They found that the choice of learner significantly impacts the complexity of the program that genetic programming must evolve. By shifting to Boolean domains and measuring nonlinearity via Fourier degree, the study demonstrated that a linear learner precisely matches the target's degree, while tree ensembles achieve higher success rates with simpler programs. This work suggests that learners can be selected based on a target's structure, and the evolved program's degree can be computed as a diagnostic metric. However, a warning is issued: increased learner capability can lead to greater program dependence and reduced legibility. AI

IMPACT This research offers a new diagnostic for understanding the complexity of problems solvable by genetic programming and the role of external learners.

RANK_REASON Academic paper detailing a novel approach to understanding and measuring evolutionary processes in genetic programming. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Genetic programming's evolution is shaped by downstream learners

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Academic paper detailing a novel approach to understanding and measuring evolutionary processes in genetic programming. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Nam H. Le ·

    Relocating Nonlinearity: How a Downstream Learner Reshapes What Genetic Programming Must Evolve

    Genetic programming was conceived as a way of evolving solutions: the program is the answer, and fitness is the error of its own output. A substantial line of work instead makes the program an input to a separate learner, so fitness measures the learner's output rather than the p…

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