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Found 23 papers, 4 papers with code
Analysis of the (1+1) EA on LeadingOnes with Constraints
Understanding how evolutionary algorithms perform on constrained problems has gained increasing attention in recent years.
Evolutionary Diversity Optimisation in Constructing Satisfying Assignments
In this study, we introduce evolutionary algorithms (EAs) employing a well-known SAT solver to maximise diversity among a set of SAT solutions explicitly.
Temporal Network Creation Games
However, many real-world networks are not shaped by a central designer but instead they emerge and evolve by the interaction of many strategic agents.
Single-Peaked Jump Schelling Games
We contribute to the recent endeavor of investigating residential segregation models with realistic agent behavior by studying Jump Schelling Games with agents having a single-peaked utility function.
Fair Correlation Clustering in Forests
To this end, we consider restricted graph classes which allow us to characterize the distributions of sensitive attributes for which this form of fairness is tractable from a complexity point of view.
Theoretical Study of Optimizing Rugged Landscapes with the cGA
Estimation of distribution algorithms (EDAs) provide a distribution - based approach for optimization which adapts its probability distribution during the run of the algorithm.
Deep Distance Sensitivity Oracles
More specifically, we utilize the combinatorial structure of replacement paths as a concatenation of shortest paths and use deep learning to find the pivot nodes for stitching shortest paths into replacement paths.
Fast Feature Selection with Fairness Constraints
We study the fundamental problem of selecting optimal features for model construction.
What's Wrong with Deep Learning in Tree Search for Combinatorial Optimization
Second, using our benchmark suite, we conduct an in-depth analysis of the popular guided tree search algorithm by Li et al. [NeurIPS 2018], testing various configurations on small and large synthetic and real-world graphs.
Towards Explainable Real Estate Valuation via Evolutionary Algorithms
Human lives are increasingly influenced by algorithms, which therefore need to meet higher standards not only in accuracy but also with respect to explainability.
What’s Wrong with Deep Learning in Tree Search for Combinatorial Optimization
Second, using our benchmark suite, we conduct an in-depth analysis of the popular guided tree search algorithm by Li et al. [NeurIPS 2018], testing various configurations on small and large synthetic and real-world graphs.
Adaptive Sampling for Fast Constrained Maximization of Submodular Function
Our algorithm is suitable to maximize a non-monotone submodular function under a $p$-system side constraint, and it achieves a $(p + O(\sqrt{p}))$-approximation for this problem, after only poly-logarithmic adaptive rounds and polynomial queries to the valuation oracle function.
timeXplain -- A Framework for Explaining the Predictions of Time Series Classifiers
Modern time series classifiers display impressive predictive capabilities, yet their decision-making processes mostly remain black boxes to the user.
Non-Monotone Submodular Maximization with Multiple Knapsacks in Static and Dynamic Settings
We show that our proposed algorithm competes with the state-of-the-art in static settings.
From Symmetry to Asymmetry: Generalizing TSP Approximations by Parametrization
Our generalization of the tree doubling algorithm gives a parameterized 3-approximation, where the parameter is the number of asymmetric edges in a given minimum spanning arborescence.
Data Structures and Algorithms
A Practical Maximum Clique Algorithm for Matching with Pairwise Constraints
An alternative approach is to directly search for the subset of correspondences that are pairwise consistent, without optimising the registration function.
Pareto Optimization for Subset Selection with Dynamic Cost Constraints
We also consider EAMC, a new evolutionary algorithm with polynomial expected time guarantee to maintain $\phi$ approximation ratio, and NSGA-II with two different population sizes as advanced multi-objective optimization algorithm, to demonstrate their challenges in optimizing the maximum coverage problem.
Approximating Optimization Problems using EAs on Scale-Free Networks
It has been observed that many complex real-world networks have certain properties, such as a high clustering coefficient, a low diameter, and a power-law degree distribution.
A Generic Bet-and-run Strategy for Speeding Up Traveling Salesperson and Minimum Vertex Cover
A common strategy for improving optimization algorithms is to restart the algorithm when it is believed to be trapped in an inferior part of the search space.
Escaping Local Optima using Crossover with Emergent or Reinforced Diversity
This proves a sizeable advantage of all variants of the ($\mu$+1) GA compared to (1+1) EA, which requires time $\Theta(n^k)$.
The Benefit of Sex in Noisy Evolutionary Search
For this, we model sexual recombination with a simple estimation of distribution algorithm called the Compact Genetic Algorithm (cGA), which we compare with the classical $\mu+1$ EA.
Seeding the Initial Population of Multi-Objective Evolutionary Algorithms: A Computational Study
We investigate the effect of two seeding techniques for five algorithms on 48 optimization problems with 2, 3, 4, 6, and 8 objectives.
Multiplicative Approximations, Optimal Hypervolume Distributions, and the Choice of the Reference Point
We consider indicator-based algorithms whose goal is to maximize the hypervolume for a given problem by distributing {\mu} points on the Pareto front.
