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Found 53 papers, 4 papers with code
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.
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.
Evolution of Images with Diversity and Constraints Using a Generator Network
To date this work has focused on the generation of images concordant with one or more classes and transfer of artistic styles.
Discrepancy-based Evolutionary Diversity Optimization
Diversity plays a crucial role in evolutionary computation.
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 Image Transition Based on Theoretical Insights of Random Processes
Evolutionary algorithms have been widely studied from a theoretical perspective.
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.
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.
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.
Optimization of Chance-Constrained Submodular Functions
In this paper, we investigate submodular optimization problems with chance constraints.
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).
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.
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.
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.
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.
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.
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.
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.
Computing Diverse Sets of Solutions for Monotone Submodular Optimisation Problems
Submodular functions allow to model many real-world optimisation problems.
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.
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.
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).
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.
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.
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.
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.
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.
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).
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.
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 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.
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.
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.
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.
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).
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.
Towards Self-adaptive Mutation in Evolutionary Multi-Objective Algorithms
Parameter control has succeeded in accelerating the convergence process of evolutionary algorithms.
Evolving Reinforcement Learning Environment to Minimize Learner's Achievable Reward: An Application on Hardening Active Directory Systems
The defender picks a specific environment configuration.
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.
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.
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.
Evolutionary Multi-Objective Diversity Optimization
Creating diverse sets of high quality solutions has become an important problem in recent years.
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.
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.
Multi-Objective Evolutionary Algorithms with Sliding Window Selection for the Dynamic Chance-Constrained Knapsack Problem
The chance-constrained problem model allows us to effectively capture the stochastic profits and associate a confidence level to the solutions' profits.
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.
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.
