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.

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 ${\displaystyle a'^{(g+1)}}$ and its parents receive a reward ${\displaystyle r^{(g)}}$, if ${\displaystyle a'^{(g+1)}}$ was selected for new population ${\displaystyle Q^{(g+1)}}$, otherwise the reward is zero. Several reward definitions are possible:

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