A diversity-enhanced genetic algorithm with a similarity penalty explores multiple viable parameter regions more efficiently than random scans and, in a toy test, finds a more diverse set of optima than SciPy's differential evolution.
Here, we discuss the four methods implemented in our algorithm: midpoint crossover, either/or crossover, between crossover, and no crossover
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
cs.NE 1years
2024 1verdicts
CONDITIONAL 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
A diversity-enhanced genetic algorithm for efficient exploration of parameter spaces
A diversity-enhanced genetic algorithm with a similarity penalty explores multiple viable parameter regions more efficiently than random scans and, in a toy test, finds a more diverse set of optima than SciPy's differential evolution.