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Found 6 papers, 0 papers with code
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.
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.
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.
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.
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.
