Based on these observations, a two-phase DE algorithm (TPDE) is proposed for minimax optimization, where one phase focuses on searching the scenario space and the other puts emphasis on the solution space. In the first phase, in order to refine promising individuals, TPDE focuses on searching the scenario space.
The two-phase differential evolution can provide outperformed or competitive performance for both benchmark and real-world minimax optimization problems.
Oct 22, 2024 · The optimum of a minimax optimization problem is the minimum of maximal outputs in all possible scenarios. A minimax optimization problem ...
The optimum of a minimax optimization problem is the minimum of maximal outputs in all possible scenarios. A minimax optimization problem includes two ...
Over the last two decades, different differential evolution (DE) variants have been successfully used to solve different optimization problems.
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We present a novel DE variant that can symmetrically decouple exploration and exploitation during the optimization process in this paper.
Aug 24, 2024 · We introduce a refined DE algorithm characterized by adaptive parameters and dual mutation strategies (APDSDE).
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In the second phase, maximizing the minimum Euclidean distance among samples is treated as another fitness function. By optimizing this fitness function, the ...
This paper introduces CoMiniMax, a coevolutionary Minimax algorithm, based on Differential Evolution, for the detection of Nash Equilibrium in games.
Oct 14, 2020 · The training phase consists of two main stages: structure optimization and weight update. The network structure is optimized during the ...
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