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Found 18 papers, 7 papers with code
Better Understandings and Configurations in MaxSAT Local Search Solvers via Anytime Performance Analysis
Though numerous solvers have been proposed for the MaxSAT problem, and the benchmark environment such as MaxSAT Evaluations provides a platform for the comparison of the state-of-the-art solvers, existing assessments were usually evaluated based on the quality, e. g., fitness, of the best-found solutions obtained within a given running time budget.
Impact of spatial transformations on landscape features of CEC2022 basic benchmark problems
When benchmarking optimization heuristics, we need to take care to avoid an algorithm exploiting biases in the construction of the used problems.
MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts
Choosing a set of benchmark problems is often a key component of any empirical evaluation of iterative optimization heuristics.
MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts
Extending a recent suggestion to generate new instances for numerical black-box optimization benchmarking by interpolating pairs of the well-established BBOB functions from the COmparing COntinuous Optimizers (COCO) platform, we propose in this work a further generalization that allows multiple affine combinations of the original instances and arbitrarily chosen locations of the global optima.
When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems
In this work, we use the notion of the resolution of continuous variables to discretize problems from the continuous domain.
Towards Self-adaptive Mutation in Evolutionary Multi-Objective Algorithms
Parameter control has succeeded in accelerating the convergence process of evolutionary algorithms.
Using Affine Combinations of BBOB Problems for Performance Assessment
By analyzing performance trajectories on more function combinations, we also show that aspects such as the scaling of objective functions and placement of the optimum can greatly impact how these results are interpreted.
Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler
Submodular functions play a key role in the area of optimization as they allow to model many real-world problems that face diminishing returns.
Non-Elitist Selection Can Improve the Performance of Irace
In addition, the obtained results indicate that non-elitist can obtain diverse algorithm configurations, which encourages us to explore a wider range of solutions to understand the behavior of algorithms.
IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics
IOHexperimenter can be used as a stand-alone tool or as part of a benchmarking pipeline that uses other components of IOHprofiler such as IOHanalyzer, the module for interactive performance analysis and visualization.
Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance
Finding the best configuration of algorithms' hyperparameters for a given optimization problem is an important task in evolutionary computation.
Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection
What complicates this decision further is that different algorithms may be best suited for different stages of the optimization process.
IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics
An R programming interface is provided for users preferring to have a finer control over the implemented functionalities.
Benchmarking a $(μ+λ)$ Genetic Algorithm with Configurable Crossover Probability
Moreover, we observe that the ``fast'' mutation scheme with its are power-law distributed mutation strengths outperforms standard bit mutation on complex optimization tasks when it is combined with crossover, but performs worse in the absence of crossover.
Benchmarking Discrete Optimization Heuristics with IOHprofiler
Automated benchmarking environments aim to support researchers in understanding how different algorithms perform on different types of optimization problems.
Interpolating Local and Global Search by Controlling the Variance of Standard Bit Mutation
We introduce in this work a simple way to interpolate between the random global search of EAs and their deterministic counterparts which sample from a fixed radius only.
IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics
Given as input algorithms and problems written in C or Python, it provides as output a statistical evaluation of the algorithms' performance by means of the distribution on the fixed-target running time and the fixed-budget function values.
Towards a Theory-Guided Benchmarking Suite for Discrete Black-Box Optimization Heuristics: Profiling $(1+λ)$ EA Variants on OneMax and LeadingOnes
Marking an important step towards filling this gap, we adjust the COCO software to pseudo-Boolean optimization problems, and obtain from this a benchmarking environment that allows a fine-grained empirical analysis of discrete black-box heuristics.
