The influence of mutation on population dynamics in multiobjective genetic programming
Created by W.Langdon from
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- @Article{Badran:2009:GPEM,
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author = "Khaled Badran and Peter I. Rockett",
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title = "The influence of mutation on population dynamics in
multiobjective genetic programming",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2010",
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volume = "11",
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number = "1",
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pages = "5--33",
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month = mar,
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keywords = "genetic algorithms, genetic programming,
Multiobjective genetic programming, Population
collapse, Mutation, Population dynamics, MOGP, bloat",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-009-9084-3",
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abstract = "Using multiobjective genetic programming with a
complexity objective to overcome tree bloat is usually
very successful but can sometimes lead to undesirable
collapse of the population to all single-node trees. In
this paper we report a detailed examination of why and
when collapse occurs. We have used different types of
crossover and mutation operators (depth-fair and
sub-tree), different evolutionary approaches
(generational and steady-state), and different datasets
(6-parity Boolean and a range of benchmark machine
learning problems) to strengthen our conclusion. We
conclude that mutation has a vital role in preventing
population collapse by counterbalancing parsimony
pressure and preserving population diversity. Also,
mutation controls the size of the generated individuals
which tends to dominate the time needed for fitness
evaluation and therefore the whole evolutionary
process. Further, the average size of the individuals
in a GP population depends on the evolutionary approach
employed. We also demonstrate that mutation has a wider
role than merely culling single-node individuals from
the population; even within a diversity-preserving
algorithm such as SPEA2 mutation has a role in
preserving diversity.",
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notes = "Steady-state algorithm depth-fair crossover/depth-fair
mutation",
- }
Genetic Programming entries for
Khaled M S Badran
Peter I Rockett
Citations