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Found 14 papers, 1 papers with code
Using the Empirical Attainment Function for Analyzing Single-objective Black-box Optimization Algorithms
A widely accepted way to assess the performance of iterative black-box optimizers is to analyze their empirical cumulative distribution function (ECDF) of pre-defined quality targets achieved not later than a given runtime.
Analysis of modular CMA-ES on strict box-constrained problems in the SBOX-COST benchmarking suite
Specifically, we want to understand the performance difference between BBOB and SBOX-COST as a function of two initialization methods and six constraint-handling strategies all tested with modular CMA-ES.
Applying Ising Machines to Multi-objective QUBOs
In this work, we extend the adaptive method based on averages in two ways: (i)~we extend the adaptive method of deriving scalarisation weights for problems with two or more objectives, and (ii)~we use an alternative measure of distance to improve performance.
Multi-Objective Archiving
Such an archive can be solely used to store high-quality solutions presented to the decision maker, but in many cases may participate in the search process (e. g., as the population in evolutionary computation).
A Study of Scalarisation Techniques for Multi-Objective QUBO Solving
These solvers are then applied to QUBO formulations of combinatorial optimisation problems.
Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration
Algorithm selection wizards are effective and versatile tools that automatically select an optimization algorithm given high-level information about the problem and available computational resources, such as number and type of decision variables, maximal number of evaluations, possibility to parallelize evaluations, etc.
Multi-objective QUBO Solver: Bi-objective Quadratic Assignment
We present the first attempt to extend the algorithm supporting a commercial QUBO solver as a multi-objective solver that is not based on scalarisation.
Are Evolutionary Algorithms Safe Optimizers?
We consider a type of constrained optimization problem, where the violation of a constraint leads to an irrevocable loss, such as breakage of a valuable experimental resource/platform or loss of human life.
The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems
Overall, by organizing this competition we have introduced routing problems as an interesting problem setting for AI researchers.
Extensible Logging and Empirical Attainment Function for IOHexperimenter
IOHexperimenter provides a large set of synthetic problems, a logging system, and a fast implementation.
Managing Manufacturing and Delivery of Personalised Medicine: Current and Future Models
To accelerate technology adoption in this domain, we characterize pertinent practical challenges in a PM supply chain and then capture them in a holistic mathematical model ready for optimisation.
Reproducibility in Evolutionary Computation
Experimental studies are prevalent in Evolutionary Computation (EC), and concerns about the reproducibility and replicability of such studies have increased in recent times, reflecting similar concerns in other scientific fields.
Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art
Safe learning and optimization deals with learning and optimization problems that avoid, as much as possible, the evaluation of non-safe input points, which are solutions, policies, or strategies that cause an irrecoverable loss (e. g., breakage of a machine or equipment, or life threat).
Local Optimal Sets and Bounded Archiving on Multi-objective NK-Landscapes with Correlated Objectives
Such local search algorithms typically return a set of mutually nondominated Pareto local optimal (PLO) solutions, that is, a PLO-set.
