A new trapdoor knapsack public-key cryptosystem
Proc. of the EUROCRYPT 84 workshop on Advances in cryptology: theory and application of cryptographic techniques
Approximate Algorithms for the 0/1 Knapsack Problem
Journal of the ACM (JACM)
Fast Approximation Algorithms for the Knapsack and Sum of Subset Problems
Journal of the ACM (JACM)
A Schema-Guiding Evolutionary Algorithm for 0-1 Knapsack Problem
IACSIT-SC '09 Proceedings of the 2009 International Association of Computer Science and Information Technology - Spring Conference
A novel multi-mutation binary particle swarm optimization for 0/1 knapsack problem
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Genetic Algorithm Based on Greedy Strategy in the 0-1 Knapsack Problem
WGEC '09 Proceedings of the 2009 Third International Conference on Genetic and Evolutionary Computing
Chemical-reaction-inspired metaheuristic for optimization
IEEE Transactions on Evolutionary Computation
Review Article: Solving 0-1 knapsack problem by a novel global harmony search algorithm
Applied Soft Computing
An Improved Genetic Algorithm for 0-1 Knapsack Problems
ICNDC '11 Proceedings of the 2011 Second International Conference on Networking and Distributed Computing
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
Linearly shift knapsack public-key cryptosystem
IEEE Journal on Selected Areas in Communications
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The 0-1 knapsack problem (KP01) is a well-known combinatorial optimization problem. It is an NP-hard problem which plays important roles in computing theory and in many real life applications. Chemical reaction optimization (CRO) is a new optimization framework, inspired by the nature of chemical reactions. CRO has demonstrated excellent performance in solving many engineering problems such as the quadratic assignment problem, neural network training, multimodal continuous problems, etc. This paper proposes a new chemical reaction optimization with greedy strategy algorithm (CROG) to solve KP01. The paper also explains the operator design and parameter turning methods for CROG. A new repair function integrating a greedy strategy and random selection is used to repair the infeasible solutions. The experimental results have proven the superior performance of CROG compared to genetic algorithm (GA), ant colony optimization (ACO) and quantum-inspired evolutionary algorithm (QEA).