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05/24/2023
Analysis of modular CMA-ES on strict box-constrained problems in the SBOX-COST benchmarking suite
Box-constraints limit the domain of decision variables and are common in...
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05/19/2023
Applying Ising Machines to Multi-objective QUBOs
Multi-objective optimisation problems involve finding solutions with var...
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03/16/2023
Multi-Objective Archiving
Most multi-objective optimisation algorithms maintain an archive explici...
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10/20/2022
A Study of Scalarisation Techniques for Multi-Objective QUBO Solving
In recent years, there has been significant research interest in solving...
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09/09/2022
Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration
Algorithm selection wizards are effective and versatile tools that autom...
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05/26/2022
Multi-objective QUBO Solver: Bi-objective Quadratic Assignment
Quantum and quantum-inspired optimisation algorithms are designed to sol...
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04/20/2022
Analyzing the Impact of Undersampling on the Benchmarking and Configuration of Evolutionary Algorithms
The stochastic nature of iterative optimization heuristics leads to inhe...
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03/24/2022
Are Evolutionary Algorithms Safe Optimizers?
We consider a type of constrained optimization problem, where the violat...
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01/25/2022
The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems
This paper reports on the first international competition on AI for the ...
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09/28/2021
Extensible Logging and Empirical Attainment Function for IOHexperimenter
In order to allow for large-scale, landscape-aware, per-instance algorit...
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02/05/2021
Reproducibility in Evolutionary Computation
Experimental studies are prevalent in Evolutionary Computation (EC), and...
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01/23/2021
Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art
Safe learning and optimization deals with learning and optimization prob...
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07/07/2020
Benchmarking in Optimization: Best Practice and Open Issues
This survey compiles ideas and recommendations from more than a dozen re...
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05/12/2020
Unified Framework for the Adaptive Operator Selection of Discrete Parameters
We conduct an exhaustive survey of adaptive selection of operators (AOS)...
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05/20/2019
Deep Reinforcement Learning Based Parameter Control in Differential Evolution
Adaptive Operator Selection (AOS) is an approach that controls discrete ...
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09/19/2014
