Reward-based selection is a technique used in evolutionary algorithms for selecting potentially useful solutions for recombination.
The probability of being selected for an individual is proportional to the cumulative reward obtained by the individual.
Reward-based selection can be used within Multi-armed bandit framework for Multi-objective optimization to obtain a better approximation of the Pareto front.
and its parents receive a reward
Several reward definitions are possible: Reward-based selection can quickly identify the most fruitful directions of search by maximizing the cumulative reward of individuals.