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From Sequential Algorithm Selection to Parallel Portfolio Selection |
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1 | (16) |
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An Algorithm Selection Benchmark of the Container Pre-marshalling Problem |
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17 | (6) |
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ADVISER: A Web-Based Algorithm Portfolio Deviser |
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23 | (6) |
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Identifying Best Hyperparameters for Deep Architectures Using Random Forests |
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29 | (14) |
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Programming by Optimisation Meets Parameterised Algorithmics: A Case Study for Cluster Editing |
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43 | (16) |
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OSCAR: Online Selection of Algorithm Portfolios with Case Study on Memetic Algorithms |
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59 | (15) |
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Learning a Hidden Markov Model-Based Hyper-heuristic |
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74 | (15) |
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Comparison of Parameter Control Mechanisms in Multi-objective Differential Evolution |
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89 | (15) |
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Genetic Programming, Logic Design and Case-Based Reasoning for Obstacle Avoidance |
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104 | (15) |
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Minimizing Total Tardiness on Identical Parallel Machines Using VNS with Learning Memory |
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119 | (6) |
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Dynamic Service Selection with Optimal Stopping and `Trivial Choice' |
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125 | (6) |
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A Comparative Study on Self-Adaptive Differential Evolution Algorithms for Test Functions and a Real-World Problem |
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131 | (6) |
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Empirical Analysis of Operators for Permutation Based Problems |
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137 | (14) |
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Fitness Landscape of the Factoradic Representation on the Permutation Flowshop Scheduling Problem |
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151 | (14) |
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Exploring Non-neutral Landscapes with Neutrality-Based Local Search |
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165 | (5) |
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A Selector Operator-Based Adaptive Large Neighborhood Search for the Covering Tour Problem |
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170 | (16) |
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Metaheuristics for the Two-Dimensional Container Pre-Marshalling Problem |
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186 | (16) |
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Improving the State of the Art in Inexact TSP Solving Using Per-Instance Algorithm Selection |
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202 | (16) |
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A Biased Random-Key Genetic Algorithm for the Multiple Knapsack Assignment Problem |
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218 | (5) |
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DYNAMOP Applied to the Unit Commitment Problem |
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223 | (6) |
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Scalarized Lower Upper Confidence Bound Algorithm |
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229 | (7) |
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Generating Training Data for Learning Linear Composite Dispatching Rules for Scheduling |
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236 | (13) |
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A Practical Case of the Multiobjective Knapsack Problem: Design, Modelling, Tests and Analysis |
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249 | (7) |
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A Bayesian Approach to Constrained Multi-objective Optimization |
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256 | (6) |
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Solving Large MultiZenoTravel Benchmarks with Divide-and-Evolve |
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262 | (6) |
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Incremental MaxSAT Reasoning to Reduce Branches in a Branch-and-Bound Algorithm for MaxClique |
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268 | (7) |
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Reusing the Same Coloring in the Child Nodes of the Search Tree for the Maximum Clique Problem |
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275 | (6) |
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A Warped Kernel Improving Robustness in Bayesian Optimization Via Random Embeddings |
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281 | (6) |
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Making EGO and CMA-ES Complementary for Global Optimization |
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287 | (6) |
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MO -- Mineclust: A Framework for Multi-objective Clustering |
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293 | (13) |
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A Software Interface for Supporting the Application of Data Science to Optimisation |
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306 | (7) |
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| Author Index |
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313 | |
