Bridging Fitness With Search Spaces By Fitness Supremums: A Theoretical Study on LGP
Created by W.Langdon from
gp-bibliography.bib Revision:1.8810
- @Misc{huang2025bridgingfitnesssearchspaces,
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author = "Zhixing Huang and Yi Mei and Fangfang Zhang and
Mengjie Zhang and Wolfgang Banzhaf",
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title = "Bridging Fitness With Search Spaces By Fitness
Supremums: A Theoretical Study on {LGP}",
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howpublished = "arXiv 2505.21991",
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year = "2025",
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month = "28 " # may,
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note = "https://doi.org/10.48550/arXiv.2505.21991",
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keywords = "genetic algorithms, genetic programming, Fitness
Supremum, Instruction Editing Distance, Bloat Effect,
Minimum Hitting Time",
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primaryclass = "cs.NE",
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URL = "
https://arxiv.org/abs/2505.21991",
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size = "30 pages",
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abstract = "... as an example to study the fitness-to-genotype
relationship. We find that the fitness expectation
increases with fitness supremum over instruction
editing distance, considering 1) the fitness supremum
linearly increases with the instruction editing
distance in LGP, 2) the fitness infimum is fixed, and
3) the fitness probabilities over different instruction
editing distances are similar. We then extend these
findings to explain the bloat effect and the minimum
hitting time of LGP based on instruction editing
distance. The bloat effect happens because it is more
likely to produce better offspring by adding
instructions than by removing them, given an
instruction editing distance from the optimal program.
The analysis of the minimum hitting time suggests that
for a basic LGP genetic operator (i.e., freemut),
maintaining a necessarily small program size and
mutating multiple instructions each time can improve
LGP performance. The reported empirical results verify
our hypothesis.",
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notes = "Centre for Data Science and Artificial Intelligence &
School of Engineering and Computer Science, Victoria
University of Wellington, Wellington, 6140, New
Zealand",
- }
Genetic Programming entries for
Zhixing Huang
Yi Mei
Fangfang Zhang
Mengjie Zhang
Wolfgang Banzhaf
Citations