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Found 27 papers, 2 papers with code
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.
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
Generating diverse populations of high quality solutions has gained interest as a promising extension to the traditional optimization tasks.
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.
Advanced Ore Mine Optimisation under Uncertainty Using Evolution
In this paper, we investigate the impact of uncertainty in advanced ore mine optimisation.
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.
Computing Diverse Sets of Solutions for Monotone Submodular Optimisation Problems
Submodular functions allow to model many real-world 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.
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.
Evolutionary Multi-Objective Optimization for the Dynamic Knapsack Problem
Evolutionary algorithms are bio-inspired algorithms that can easily adapt to changing environments.
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.
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.
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.
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.
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.