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Found 110 papers, 10 papers with code
Sampling-based Pareto Optimization for Chance-constrained Monotone Submodular Problems
Its results are compared with those from other algorithms using different surrogate functions.
Analysis of Evolutionary Diversity Optimisation for the Maximum Matching Problem
For complete bipartite graphs, our runtime analysis shows that, with a reasonably small $\mu$, the $(\mu+1)$-EA achieves maximal diversity with an expected runtime of $O(\mu^2 m^4 \log(m))$ for the small gap case (where the population size $\mu$ is less than the difference in the sizes of the bipartite partitions) and $O(\mu^2 m^2 \log(m))$ otherwise.
Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes, TrailingZeros) Problem
The diversity optimization is the class of optimization problems, in which we aim at finding a diverse set of good solutions.
Runtime Analyses of NSGA-III on Many-Objective Problems
NSGA-II and NSGA-III are two of the most popular evolutionary multi-objective algorithms used in practice.
Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem
We introduce a 3-objective formulation that is able to deal with the stochastic and dynamic components at the same time and is independent of the confidence level required for the constraint.
A Block-Coordinate Descent EMO Algorithm: Theoretical and Empirical Analysis
We consider whether conditions exist under which block-coordinate descent is asymptotically efficient in evolutionary multi-objective optimization, addressing an open problem.
Evolutionary Multi-Objective Diversity Optimization
Creating diverse sets of high quality solutions has become an important problem in recent years.
A Hybrid Intelligent Framework for Maximising SAG Mill Throughput: An Integration of Expert Knowledge, Machine Learning and Evolutionary Algorithms for Parameter Optimisation
This study introduces a hybrid intelligent framework leveraging expert knowledge, machine learning techniques, and evolutionary algorithms to address this research need.
Enhanced Genetic Programming Models with Multiple Equations for Accurate Semi-Autogenous Grinding Mill Throughput Prediction
This study introduces an enhanced GP approach entitled multi-equation GP (MEGP) for more accurate prediction of SAG mill throughput.
A Study of Fitness Gains in Evolving Finite State Machines
Among the wide variety of evolutionary computing models, Finite State Machines (FSMs) have several attractions for fundamental research.
Optimizing Chance-Constrained Submodular Problems with Variable Uncertainties
Chance constraints are frequently used to limit the probability of constraint violations in real-world optimization problems where the constraints involve stochastic components.
Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax
The evolutionary diversity optimization aims at finding a diverse set of solutions which satisfy some constraint on their fitness.
On the Impact of Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem
Evolutionary algorithms have been shown to obtain good solutions for complex optimization problems in static and dynamic environments.
Improving Confidence in Evolutionary Mine Scheduling via Uncertainty Discounting
The new method discounts profits based on uncertainty within an evolutionary algorithm, sacrificing economic optimality of a single geological model for improving the downside risk over an ensemble of equally likely models.
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.
Fast Pareto Optimization Using Sliding Window Selection
Pareto optimization using evolutionary multi-objective algorithms has been widely applied to solve constrained submodular optimization problems.
3-Objective Pareto Optimization for Problems with Chance Constraints
Evolutionary multi-objective algorithms have successfully been used in the context of Pareto optimization where a given constraint is relaxed into an additional objective.
Evolving Reinforcement Learning Environment to Minimize Learner's Achievable Reward: An Application on Hardening Active Directory Systems
The defender picks a specific environment configuration.
Towards Self-adaptive Mutation in Evolutionary Multi-Objective Algorithms
Parameter control has succeeded in accelerating the convergence process of evolutionary algorithms.
Evolutionary Multi-Objective Algorithms for the Knapsack Problems with Stochastic Profits
We consider a version of the knapsack problem with stochastic profits to guarantee a certain level of confidence in the profit of the solutions.
Limited Query Graph Connectivity Test
Given a source s and a destination t, we aim to test s-t connectivity by identifying either a path (consisting of only On edges) or a cut (consisting of only Off edges).
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.
Runtime Performance of Evolutionary Algorithms for the Chance-constrained Makespan Scheduling Problem
In addition, we investigate the experimental performance of the two algorithms for the two variants.
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.
Runtime Analysis of the (1+1) EA on Weighted Sums of Transformed Linear Functions
Linear functions play a key role in the runtime analysis of evolutionary algorithms and studies have provided a wide range of new insights and techniques for analyzing evolutionary computation methods.
Analysis of Quality Diversity Algorithms for the Knapsack Problem
Quality diversity (QD) algorithms have been shown to be very successful when dealing with problems in areas such as robotics, games and combinatorial optimization.
Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem
Recently different evolutionary computation approaches have been developed that generate sets of high quality diverse solutions for a given optimisation problem.
Computing High-Quality Solutions for the Patient Admission Scheduling Problem using Evolutionary Diversity Optimisation
Diversification in a set of solutions has become a hot research topic in the evolutionary computation community.
Evolutionary Time-Use Optimization for Improving Children's Health Outcomes
We then investigate the performance of evolutionary algorithms to optimize time use for four individual health outcomes with hypothetical children with different day structures.
Evolutionary Algorithms for Limiting the Effect of Uncertainty for the Knapsack Problem with Stochastic Profits
We examine simple evolutionary algorithms and the use of heavy tail mutation and a problem-specific crossover operator for optimizing uncertain profits.
Coevolutionary Pareto Diversity Optimization
Our new Pareto Diversity optimization approach uses this bi-objective formulation to optimize the problem while also maintaining an additional population of high quality solutions for which diversity is optimized with respect to a given diversity measure.
The Compact Genetic Algorithm Struggles on Cliff Functions
We point out that the cGA faces major difficulties when solving the CLIFF function and investigate its dynamics both experimentally and theoretically around the cliff.
Defending Active Directory by Combining Neural Network based Dynamic Program and Evolutionary Diversity Optimisation
We show that the problem is #P-hard and, therefore, intractable to solve exactly.
Evolutionary Diversity Optimisation for The Traveling Thief Problem
There has been a growing interest in the evolutionary computation community to compute a diverse set of high-quality solutions for a given optimisation problem.
Exploring the Feature Space of TSP Instances Using Quality Diversity
Generating instances of different properties is key to algorithm selection methods that differentiate between the performance of different solvers for a given combinatorial optimization problem.
Niching-based Evolutionary Diversity Optimization for the Traveling Salesperson Problem
In this work, we consider the problem of finding a set of tours to a traveling salesperson problem (TSP) instance maximizing diversity, while satisfying a given cost constraint.
Practical Fixed-Parameter Algorithms for Defending Active Directory Style Attack Graphs
The other assumes a small number of splitting nodes (nodes with multiple out-going edges).
Run-of-Mine Stockyard Recovery Scheduling and Optimisation for Multiple Reclaimers
Stockpiles are essential in the mining value chain, assisting in maximising value and production.
On the Use of Quality Diversity Algorithms for The Traveling Thief Problem
In real-world optimisation, it is common to face several sub-problems interacting and forming the main problem.
Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems
Topology optimisation of trusses can be formulated as a combinatorial and multi-modal problem in which locating distinct optimal designs allows practitioners to choose the best design based on their preferences.
Time Complexity Analysis of Evolutionary Algorithms for 2-Hop (1,2)-Minimum Spanning Tree Problem
Following how evolutionary algorithms are applied to solve the MSTP, we first consider the evolutionary algorithms with search points in edge-based representation adapted to the 2H-(1, 2)-MSTP (including the (1+1) EA, Global Simple Evolutionary Multi-Objective Optimizer and its two variants).
Runtime Analysis of Single- and Multi-Objective Evolutionary Algorithms for Chance Constrained Optimization Problems with Normally Distributed Random Variables
With this paper, we contribute to the theoretical understanding of evolutionary algorithms for chance constrained optimization.
Computing Diverse Sets of High Quality TSP Tours by EAX-Based Evolutionary Diversity Optimisation
In this paper, we introduce evolutionary diversity optimisation (EDO) approaches for the TSP that find a diverse set of tours when the optimal tour is known or unknown.
Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem
In most settings the expected reoptimization time for such tailored algorithms is linear in the number of added edges.
Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem
Computing diverse sets of high-quality solutions has gained increasing attention among the evolutionary computation community in recent years.
Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms
In practise, it is often desirable to provide the decision-maker with a rich set of diverse solutions of decent quality instead of just a single solution.
Heuristic Strategies for Solving Complex Interacting Large-Scale Stockpile Blending Problems
Besides, we introduce a multi-component fitness function for solving the large-scale stockpile blending problem which can maximize the volume of metal over the plan and maintain the balance between stockpiles according to the usage of metal.
Analysis of Evolutionary Diversity Optimisation for Permutation Problems
This work contributes to this line of research with an investigation on evolutionary diversity optimization for three of the most well-studied permutation problems, namely the Traveling Salesperson Problem (TSP), both symmetric and asymmetric variants, and Quadratic Assignment Problem (QAP).
Runtime Analysis of RLS and the (1+1) EA for the Chance-constrained Knapsack Problem with Correlated Uniform Weights
We perform runtime analysis of a randomized search algorithm (RSA) and a basic evolutionary algorithm (EA) for the chance-constrained knapsack problem with correlated uniform weights.
Heuristic Strategies for Solving Complex Interacting Stockpile Blending Problem with Chance Constraints
In this paper, we consider the uncertainty in material grades and introduce chance constraints that are used to ensure the constraints with high confidence.
Advanced Ore Mine Optimisation under Uncertainty Using Evolution
In this paper, we investigate the impact of uncertainty in advanced ore mine optimisation.
Pareto Optimization for Subset Selection with Dynamic Partition Matroid Constraints
In this study, we consider the subset selection problems with submodular or monotone discrete objective functions under partition matroid constraints where the thresholds are dynamic.
Atiyah sequences and connections on principal bundles over differentiable stacks
We construct and study general connections on Lie groupoids and differentiable stacks as well as on principal bundles over them using Atiyah sequences associated to transversal tangential distributions.
Differential Geometry Category Theory Primary 53C08, Secondary 22A22, 58H05, 53D50
Connections on Lie groupoids and Chern-Weil theory
Let $\mathbb{X}=[X_1\rightrightarrows X_0]$ be a Lie groupoid equipped with a connection, given by a smooth distribution $\mathcal{H} \subset T X_1$ transversal to the fibers of the source map.
Differential Geometry Category Theory Primary 53C08, Secondary 22A22, 58H05, 53D50
Computing Diverse Sets of Solutions for Monotone Submodular Optimisation Problems
Submodular functions allow to model many real-world optimisation problems.
Improved Runtime Results for Simple Randomised Search Heuristics on Linear Functions with a Uniform Constraint
Linear functions have been traditionally studied in this area resulting in tight bounds on the expected optimisation time of simple randomised search algorithms for this class of problems.
Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem
In the area of evolutionary computation the calculation of diverse sets of high-quality solutions to a given optimization problem has gained momentum in recent years under the term evolutionary diversity optimization.
Using Neural Networks and Diversifying Differential Evolution for Dynamic Optimisation
Considering the complexity of using neural networks in the process compared to simple diversity mechanisms, we investigate whether they are competitive and the possibility of integrating them to improve the results.
A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization
The theory of evolutionary computation for discrete search spaces has made significant progress in the last ten years.
Maximizing Submodular or Monotone Functions under Partition Matroid Constraints by Multi-objective Evolutionary Algorithms
Many important problems can be regarded as maximizing submodular functions under some constraints.
Optimising Monotone Chance-Constrained Submodular Functions Using Evolutionary Multi-Objective Algorithms
We show that the GSEMO algorithm obtains the same worst case performance guarantees as recently analyzed greedy algorithms.
Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions
The Traveling Salesperson Problem (TSP) is one of the best-known combinatorial optimisation problems.
More Effective Randomized Search Heuristics for Graph Coloring Through Dynamic Optimization
We show that EAs can solve the graph coloring problem for bipartite graphs more efficiently by using dynamic optimization.
Single- and Multi-Objective Evolutionary Algorithms for the Knapsack Problem with Dynamically Changing Constraints
Evolutionary algorithms are bio-inspired algorithms that can easily adapt to changing environments.
The Dynamic Travelling Thief Problem: Benchmarks and Performance of Evolutionary Algorithms
Many real-world optimisation problems involve dynamic and stochastic components.
Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem
We consider the Minimum Spanning Tree (MST) problem in a single- and multi-objective version, and introduce a biased mutation, which puts more emphasis on the selection of edges of low rank in terms of low domination number.
Evolving Diverse Sets of Tours for the Travelling Salesperson Problem
Evolving diverse sets of high quality solutions has gained increasing interest in the evolutionary computation literature in recent years.
Specific Single- and Multi-Objective Evolutionary Algorithms for the Chance-Constrained Knapsack Problem
We use this model in combination with the problem-specific crossover operator in multi-objective evolutionary algorithms to solve the problem.
Evolutionary Image Transition and Painting Using Random Walks
We introduce an evolutionary image painting approach whose underlying biased random walk can be controlled by a parameter influencing the bias of the random walk and thereby creating different artistic painting effects.
Evolutionary Bi-objective Optimization for the Dynamic Chance-Constrained Knapsack Problem Based on Tail Bound Objectives
In this paper, we consider the dynamic chance-constrained knapsack problem where the weight of each item is stochastic, the capacity constraint changes dynamically over time, and the objective is to maximize the total profit subject to the probability that total weight exceeds the capacity.
The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics
In this paper, we investigate the effect of weights on such problems, in the sense that the cost of traveling increases with respect to the weights of nodes already visited during a tour.
Runtime Performances of Randomized Search Heuristics for the Dynamic Weighted Vertex Cover Problem
Randomized search heuristics such as evolutionary algorithms are frequently applied to dynamic combinatorial optimization problems.
Neural Networks in Evolutionary Dynamic Constrained Optimization: Computational Cost and Benefits
As NN needs to collect data at each time step, if the time horizon is short, we will not be able to collect enough samples to train the NN.
Parameterized Complexity Analysis of Randomized Search Heuristics
This chapter compiles a number of results that apply the theory of parameterized algorithmics to the running-time analysis of randomized search heuristics such as evolutionary algorithms.
One-Shot Decision-Making with and without Surrogates
We study three different decision tasks: classic one-shot optimization (only the best sample matters), one-shot optimization with surrogates (allowing to use surrogate models for selecting a design that need not necessarily be one of the evaluated samples), and one-shot regression (i. e., function approximation, with minimization of mean squared error as objective).
Optimization of Chance-Constrained Submodular Functions
In this paper, we investigate submodular optimization problems with chance constraints.
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.
On the Use of Diversity Mechanisms in Dynamic Constrained Continuous Optimization
Population diversity plays a key role in evolutionary algorithms that enables global exploration and avoids premature convergence.
Runtime Analysis of RLS and (1+1) EA for the Dynamic Weighted Vertex Cover Problem
Similar to the classical case, the dynamic changes that we consider on the weighted vertex cover problem are adding and removing edges to and from the graph.
On the Behaviour of Differential Evolution for Problems with Dynamic Linear Constraints
Our proposed framework creates dynamic benchmarks that are flexible in terms of number of changes, dimension of the problem and can be applied to test any objective function.
Analysis of Baseline Evolutionary Algorithms for the Packing While Travelling Problem
In this paper, variations of the Packing While Travelling (PWT) -- also known as the non-linear knapsack problem -- are studied as an attempt to analyse the behaviour of EAs on non-linear problems from theoretical perspective.
Evolutionary Algorithms for the Chance-Constrained Knapsack Problem
In the experiment section, we evaluate and compare the deterministic approaches and evolutionary algorithms on a wide range of instances.
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.
Fast Re-Optimization via Structural Diversity
We propose a simple diversity mechanism that prevents this behavior, thereby reducing the re-optimization time for LeadingOnes to $O(\gamma\delta n)$, where $\gamma$ is the population size used by the diversity mechanism and $\delta \le \gamma$ the Hamming distance of the new optimum from the previous solution.
Automated Algorithm Selection: Survey and Perspectives
The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning.
Evolutionary Diversity Optimization Using Multi-Objective Indicators
Evolutionary diversity optimization aims to compute a diverse set of solutions where all solutions meet a given quality criterion.
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.
Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments
Many real-world optimization problems occur in environments that change dynamically or involve stochastic components.
Design and Analysis of Diversity-Based Parent Selection Schemes for Speeding Up Evolutionary Multi-objective Optimisation
We show that stagnation might occur when favouring individuals with a high diversity contribution in the parent selection step and provide a discussion on which scheme to use for more complex problems based on our theoretical and experimental results.
Robust Fitting in Computer Vision: Easy or Hard?
Robust model fitting plays a vital role in computer vision, and research into algorithms for robust fitting continues to be active.
A Comparison of Constraint Handling Techniques for Dynamic Constrained Optimization Problems
Dynamic constrained optimization problems (DCOPs) have gained researchers attention in recent years because a vast majority of real world problems change over time.
Discrepancy-based Evolutionary Diversity Optimization
Diversity plays a crucial role in evolutionary computation.
Evolutionary Computation plus Dynamic Programming for the Bi-Objective Travelling Thief Problem
This research proposes a novel indicator-based hybrid evolutionary approach that combines approximate and exact algorithms.
Exact Approaches for the Travelling Thief Problem
Many evolutionary and constructive heuristic approaches have been introduced in order to solve the Traveling Thief Problem (TTP).
Evolutionary Image Composition Using Feature Covariance Matrices
This approach is very flexible in that it can work with a wide range of features and enables targeting specific regions in the images.
The Evolutionary Process of Image Transition in Conjunction with Box and Strip Mutation
Evolutionary algorithms have been used in many ways to generate digital art.
Evolutionary computation for multicomponent problems: opportunities and future directions
Over the past 30 years many researchers in the field of evolutionary computation have put a lot of effort to introduce various approaches for solving hard problems.
Guaranteed Outlier Removal With Mixed Integer Linear Programs
Towards the goal of solving maximum consensus exactly, we present guaranteed outlier removal as a technique to reduce the runtime of exact algorithms.
Evolutionary Image Transition Based on Theoretical Insights of Random Processes
Evolutionary algorithms have been widely studied from a theoretical perspective.
Parameterized Analysis of Multi-objective Evolutionary Algorithms and the Weighted Vertex Cover Problem
A rigorous runtime analysis of evolutionary multi-objective optimization for the classical vertex cover problem in the context of parameterized complexity analysis has been presented by Kratsch and Neumann (2013).
A Feature-Based Prediction Model of Algorithm Selection for Constrained Continuous Optimisation
In this research area, problem instances are classified according to different features of the underlying problem in terms of their difficulty of being solved by a particular algorithm.
Feature-Based Diversity Optimization for Problem Instance Classification
Understanding the behaviour of heuristic search methods is a challenge.
A Feature-Based Comparison of Evolutionary Computing Techniques for Constrained Continuous Optimisation
Evolutionary algorithms have been frequently applied to constrained continuous optimisation problems.
A Feature-Based Analysis on the Impact of Set of Constraints for e-Constrained Differential Evolution
We carry out a feature-based analysis of evolved constrained continuous optimization instances to understand the characteristics of constraints that make problems hard for evolutionary algorithm.
Optimising Spatial and Tonal Data for PDE-based Inpainting
Moreover, is more generic than other data optimisation approaches for the sparse inpainting problem, since it can also be extended to nonlinear inpainting operators such as EED.
On the Runtime of Randomized Local Search and Simple Evolutionary Algorithms for Dynamic Makespan Scheduling
Evolutionary algorithms have been frequently used for dynamic optimization problems.
A Parameterized Complexity Analysis of Bi-level Optimisation with Evolutionary Algorithms
For the generalised minimum spanning tree problem, we analyse the two approaches presented by Hu and Raidl (2012) with respect to the number of clusters that distinguish each other by the chosen representation of possible solutions.
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.
Evolving Pacing Strategies for Team Pursuit Track Cycling
Team pursuit track cycling is a bicycle racing sport held on velodromes and is part of the Summer Olympics.
