Population diversity and inheritance in genetic programming for symbolic regression
Created by W.Langdon from
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- @Article{burlacu:NC,
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author = "Bogdan Burlacu and Kaifeng Yang and
Michael Affenzeller",
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title = "Population diversity and inheritance in genetic
programming for symbolic regression",
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journal = "Natural Computing",
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keywords = "genetic algorithms, genetic programming, XAI, Symbolic
regression, Supervised learning, Explainable AI,
Optimization algorithm, Evolutionary algorithm",
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URL = "https://rdcu.be/c7n0f",
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URL = "http://link.springer.com/article/10.1007/s11047-022-09934-x",
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DOI = "doi:10.1007/s11047-022-09934-x",
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size = "36 pages",
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abstract = "we aim to empirically characterize two important
dynamical aspects of GP search: the evolution of
diversity and the propagation of inheritance patterns.
Diversity is calculated at the genotypic and phenotypic
levels using efficient similarity metrics. Inheritance
information is obtained via a full genealogical record
of evolution as a directed acyclic graph and a set of
methods for extracting relevant patterns. Advances in
processing power enable our approach to handle
previously infeasible graph sizes of millions of arcs
and vertices. To enable a more comprehensive analysis
we employ three closely-related but different
evolutionary models: canonical GP, offspring selection
and age-layered population structure. Our analysis
reveals that a relatively small number of ancestors are
responsible for producing the majority of descendants
in later generations, leading to diversity loss. We
show empirically across a selection of five benchmark
problems that each configuration is characterised by
different rates of diversity loss and different
inheritance patterns, in support of the idea that each
new problem may require a unique approach to solve
optimally.",
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notes = "HEAL, University of Applied Sciences Upper
Austria,Software park 11, 4232 Hagenberg, Austria",
- }
Genetic Programming entries for
Bogdan Burlacu
Kaifeng Yang
Michael Affenzeller
Citations