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Found 4 papers, 1 papers with code
Neural Networks in Evolutionary Dynamic Constrained Optimization: Computational Cost and Benefits
As NN needs to collect data at each time step, if the time horizon is short, we will not be able to collect enough samples to train the NN.
On the Use of Diversity Mechanisms in Dynamic Constrained Continuous Optimization
Population diversity plays a key role in evolutionary algorithms that enables global exploration and avoids premature convergence.
On the Behaviour of Differential Evolution for Problems with Dynamic Linear Constraints
Our proposed framework creates dynamic benchmarks that are flexible in terms of number of changes, dimension of the problem and can be applied to test any objective function.
A Comparison of Constraint Handling Techniques for Dynamic Constrained Optimization Problems
Dynamic constrained optimization problems (DCOPs) have gained researchers attention in recent years because a vast majority of real world problems change over time.
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