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Found 11 papers, 3 papers with code
NK Hybrid Genetic Algorithm for Clustering
Mutation operators, a partition crossover, and a local search strategy are proposed, all using information about the relationship between decision variables.
Dynastic Potential Crossover Operator
In this paper, we present a recombination operator, called Dynastic Potential Crossover (DPX), that runs in polynomial time and behaves like an optimal recombination operator for low-epistasis combinatorial problems.
Stochastic Subnetwork Annealing: A Regularization Technique for Fine Tuning Pruned Subnetworks
Pruning methods have recently grown in popularity as an effective way to reduce the size and computational complexity of deep neural networks.
A Parallel Ensemble of Metaheuristic Solvers for the Traveling Salesman Problem
We see that the performance of the solver varies with the type of the problem.
Randomly Initialized Subnetworks with Iterative Weight Recycling
In addition to Iterative Weight Recycling, we complement the Multi-Prize Lottery Ticket Hypothesis with a reciprocal finding: high-accuracy, randomly initialized subnetwork's produce diverse masks, despite being generated with the same hyperparameter's and pruning strategy.
Sparse Mutation Decompositions: Fine Tuning Deep Neural Networks with Subspace Evolution
Neuroevolution is a promising area of research that combines evolutionary algorithms with neural networks.
Interpretable Diversity Analysis: Visualizing Feature Representations In Low-Cost Ensembles
Diversity is an important consideration in the construction of robust neural network ensembles.
Synaptic Stripping: How Pruning Can Bring Dead Neurons Back To Life
Rectified Linear Units (ReLU) are the default choice for activation functions in deep neural networks.
Prune and Tune Ensembles: Low-Cost Ensemble Learning With Sparse Independent Subnetworks
We then create child networks by cloning the parent and dramatically pruning the parameters of each child to create an ensemble of members with unique and diverse topologies.
Efficient Hill-Climber for Multi-Objective Pseudo-Boolean Optimization
Local search can be a stand along search methods, but it can also be hybridized with evolutionary algorithms.
Optimal Neuron Selection: NK Echo State Networks for Reinforcement Learning
This paper introduces the NK Echo State Network.
