55 citations · 148 across the 16 of their papers we have counts for
16 papers
Generalizing and Unifying Gray-box Combinatorial Optimization Operators
Francisco Chicano, Darrell Whitley, Gabriela Ochoa +1
Gray-box optimization leverages the information available about the mathematical structure of an optimization problem to design efficient search operators. Efficient hill climbers…
Dancing to the State of the Art? How Candidate Lists Influence LKH for Solving the Traveling Salesperson Problem
Jonathan Heins, Lennart Schäpermeier, Pascal Kerschke +1
Solving the Traveling Salesperson Problem (TSP) remains a persistent challenge, despite its fundamental role in numerous generalized applications in modern contexts. Heuristic solv…
Dynastic Potential Crossover Operator
Francisco Chicano, Gabriela Ochoa, Darrell Whitley +1
An optimal recombination operator for two parent solutions provides the best solution among those that take the value for each variable from one of the parents (gene transmission p…
NK Hybrid Genetic Algorithm for Clustering
Renato Tinós, Liang Zhao, Francisco Chicano +1
The NK hybrid genetic algorithm for clustering is proposed in this paper. In order to evaluate the solutions, the hybrid algorithm uses the NK clustering validation criterion 2 (NK…
Stochastic Subnetwork Annealing: A Regularization Technique for Fine Tuning Pruned Subnetworks
Tim Whitaker, Darrell Whitley
Pruning methods have recently grown in popularity as an effective way to reduce the size and computational complexity of deep neural networks. Large numbers of parameters can be re…
A Parallel Ensemble of Metaheuristic Solvers for the Traveling Salesman Problem
Swetha Varadarajan, Darrell Whitley
The travelling salesman problem (TSP) is one of the well-studied NP-hard problems in the literature. The state-of-the art inexact TSP solvers are the Lin-Kernighan-Helsgaun (LKH) h…
Randomly Initialized Subnetworks with Iterative Weight Recycling
Matt Gorbett, Darrell Whitley
The Multi-Prize Lottery Ticket Hypothesis posits that randomly initialized neural networks contain several subnetworks that achieve comparable accuracy to fully trained models of t…
Sparse Mutation Decompositions: Fine Tuning Deep Neural Networks with Subspace Evolution
Tim Whitaker, Darrell Whitley
Neuroevolution is a promising area of research that combines evolutionary algorithms with neural networks. A popular subclass of neuroevolutionary methods, called evolution strateg…
Interpretable Diversity Analysis: Visualizing Feature Representations In Low-Cost Ensembles
Tim Whitaker, Darrell Whitley
Diversity is an important consideration in the construction of robust neural network ensembles. A collection of well trained models will generalize better if they are diverse in th…
Synaptic Stripping: How Pruning Can Bring Dead Neurons Back To Life
Tim Whitaker, Darrell Whitley
Rectified Linear Units (ReLU) are the default choice for activation functions in deep neural networks. While they demonstrate excellent empirical performance, ReLU activations can…
Prune and Tune Ensembles: Low-Cost Ensemble Learning With Sparse Independent Subnetworks
Tim Whitaker, Darrell Whitley
Ensemble Learning is an effective method for improving generalization in machine learning. However, as state-of-the-art neural networks grow larger, the computational cost associat…
Efficient Hill-Climber for Multi-Objective Pseudo-Boolean Optimization
Francisco Chicano, Darrell Whitley, Renato Tinos
Local search algorithms and iterated local search algorithms are a basic technique. Local search can be a stand along search methods, but it can also be hybridized with evolutionar…
Optimal Neuron Selection: NK Echo State Networks for Reinforcement Learning
Darrell Whitley, Renato Tinós, Francisco Chicano
This paper introduces the NK Echo State Network. The problem of learning in the NK Echo State Network is reduced to the problem of optimizing a special form of a Spin Glass Problem…
Fitness Probability Distribution of Bit-Flip Mutation
Francisco Chicano, Andrew M. Sutton, L. Darrell Whitley +1
Bit-flip mutation is a common mutation operator for evolutionary algorithms applied to optimize functions over binary strings. In this paper, we develop results from the theory of…
Evolutionary Hessian Learning: Forced Optimal Covariance Adaptive Learning (FOCAL)
Ofer M. Shir, Jonathan Roslund, Darrell Whitley +1
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) has been the most successful Evolution Strategy at exploiting covariance information; it uses a form of Principle Compo…
Understanding Algorithm Performance on an Oversubscribed Scheduling Application
L. Barbulescu, A. E. Howe, M. Roberts +1
The best performing algorithms for a particular oversubscribed scheduling application, Air Force Satellite Control Network (AFSCN) scheduling, appear to have little in common. Yet,…