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Everything about 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. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Reward-based selectionA 5-minute orientation
  2. Skim a recent paper: A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward FormulationInfluential work since 2022
  3. Pick up a classic book: PsychologyThe most-reprinted book on the subject
  4. Read the landmark paper: Role for Cingulate Motor Area Cells in Voluntary Movement Selection Based on RewardCited 575 times
  5. Go sideways: Hypervolume indicatorA closely related topic
  6. Go sideways: Multi-objective optimizationA closely related topic

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🏆 Landmark papers (most cited)

🆕 Influential since 2022

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