Single- and multi-objective evolutionary algorithms for the knapsack problem with dynamically changing constraints

Author:

Roostapour Vahid,Neumann AnetaORCID,Neumann Frank

Funder

Australian Research Council

Government of South Australia

Publisher

Elsevier BV

Subject

General Computer Science,Theoretical Computer Science

Reference28 articles.

1. Introduction to Evolutionary Computing;Eiben,2015

2. Continuous dynamic constrained optimization: the challenges;Nguyen;IEEE Trans. Evol. Comput.,2012

3. Noisy evolutionary optimization algorithms - a comprehensive survey;Rakshit;Swarm Evol. Comput.,2017

4. Optimization of chance-constrained submodular functions;Doerr,2020

5. Optimising monotone chance-constrained submodular functions using evolutionary multi-objective algorithms;Neumann,2020

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1. Multi-Objective Evolutionary Algorithms with Sliding Window Selection for the Dynamic Chance-Constrained Knapsack Problem;Proceedings of the Genetic and Evolutionary Computation Conference;2024-07-14

2. Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem;Proceedings of the Genetic and Evolutionary Computation Conference;2024-07-14

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4. Multi-objective Evolutionary Approaches for the Knapsack Problem with Stochastic Profits;Lecture Notes in Computer Science;2024

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