  Swarm intelligence algorithms have traditionally been designed for continuous
optimization problems, and these algorithms have been modified and extended for
application to discrete optimization problems. Notably, their application in
feature selection for machine learning has demonstrated improvements in model
accuracy, reduction of unnecessary data, and decreased computational time. This
study proposes the Frog-Snake prey-predation Relationship Optimization (FSRO)
algorithm, inspired by the prey-predation relationship between frogs and snakes
for application to discrete optimization problems. The algorithm models three
stages of a snake's foraging behavior "search", "approach", and "capture" as
well as the frog's characteristic behavior of staying still to attract and then
escaping. Furthermore, the introduction of the concept of evolutionary game
theory enables dynamic control of the search process. The proposed algorithm
conducts computational experiments on feature selection using 26 types of
machine learning datasets to analyze its performance and identify improvements.
In computer experiments, the proposed algorithm showed better performance than
the comparison algorithms in terms of the best and standard deviation of
fitness value and Accuracy. It was also proved that dynamic search control by
evolutionary game theory is an effective method, and the proposed algorithm has
the ability of a well-balanced search, achieving the two objectives of
improving accuracy and reducing data.
