Improving genetic algorithms performance via deterministic population shrinkage
GECCO(2024)
摘要
Despite the intuition that the same population size is not needed throughout
the run of an Evolutionary Algorithm (EA), most EAs use a fixed population
size. This paper presents an empirical study on the possible benefits of a
Simple Variable Population Sizing (SVPS) scheme on the performance of Genetic
Algorithms (GAs). It consists in decreasing the population for a GA run
following a predetermined schedule, configured by a speed and a severity
parameter. The method uses as initial population size an estimation of the
minimum size needed to supply enough building blocks, using a fixed-size
selectorecombinative GA converging within some confidence interval toward good
solutions for a particular problem. Following this methodology, a scalability
analysis is conducted on deceptive, quasi-deceptive, and non-deceptive trap
functions in order to assess whether SVPS-GA improves performances compared to
a fixed-size GA under different problem instances and difficulty levels.
Results show several combinations of speed-severity where SVPS-GA preserves the
solution quality while improving performances, by reducing the number of
evaluations needed for success.
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