Reward-based selection
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. The cumulative reward can be computed as a sum of the individual reward and the reward inherited from parents. Description
Reward-based selection can be used within Multi-armed bandit framework for Multi-objective optimization to obtain a better approximation of the Pareto front. The newborn  and its parents receive a reward , if  was selected for new population , otherwise the reward is zero. Several reward definitions are possible:

1. , if the newborn individual  was selected for new population . 2. , where  is the rank of newly inserted individual in the population of  individuals. Rank can be computed using a well-known non-dominated sorting procedure. 3. , where  is the hypervolume indicator contribution of the individual  to the population . The reward  if the newly inserted individual improves the quality of the population, which is measured as its hypervolume contribution in the objective space. 4. A relaxation of the above reward, involving a rank-based penalization for points for -th dominated Pareto front: 

Reward-based selection can quickly identify the most fruitful directions of search by maximizing the cumulative reward of individuals. See also
Fitness proportionate selection
Selection (genetic algorithm)
Stochastic universal sampling
Tournament selection

References 

Evolutionary algorithms
Genetic algorithms