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Evolutionary Algorithm

Metaheuristic inspired by biological evolution using genetic operators such as selection, crossover, and mutation to explore the search space of optimal solutions.

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Hypervolume

Performance indicator measuring the volume of the objective space dominated by a set of non-dominated solutions, evaluating both convergence and diversity on the Pareto front.

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Multi-Objective Policy Gradient

Reinforcement learning method that directly optimizes policy parameters to maximize a multi-objective reward vector using stochastic gradient techniques.

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Evolutionary Archive

Data structure that preserves non-dominated solutions discovered during evolution, used to maintain the history of Pareto front approximations and guide future search.

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Multi-Objective Adaptive Mutation

Mutation operator whose parameters adapt dynamically based on population diversity and convergence to balance exploration and exploitation in the multi-objective search space.

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Multi-Objective Tournament Selection

Selection mechanism in evolutionary algorithms where candidates are compared based on dominance or diversity criteria to determine individuals participating in reproduction.

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Scalarizing Aggregation Function

Technique that transforms a multi-objective problem into a single-objective problem by linearly or non-linearly combining different objectives with variable weighting coefficients.

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Evolutionary Diversity

Ability of an evolutionary algorithm to maintain a balanced distribution of solutions on the Pareto front, avoiding premature convergence to localized regions of the search space.

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Evolutionary Convergence

The tendency of an evolutionary algorithm to approach the true Pareto front over generations, measured by the distance between obtained solutions and optimal non-dominated solutions.

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