Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
A new evolutionary approach to cutting stock problems with and without contiguity
Computers and Operations Research
Genetic Algorithms for Cutting Stock Problems: With and Without Contiguity
AI '93/AI '94 Selected papers from the AI'93 and AI'94 Workshops on Evolutionary Computation, Process in Evolutionary Computation
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
A hybrid heuristic to reduce the number of different patterns in cutting stock problems
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
A stabilized branch-and-price-and-cut algorithm for the multiple length cutting stock problem
Computers and Operations Research
Heuristic algorithm for a cutting stock problem in the steel bridge construction
Computers and Operations Research
Heuristics for the one-dimensional cutting stock problem with limited multiple stock lengths
Computers and Operations Research
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 07
Solving One-dimensional Cutting-Stock Problem Based on Ant Colony Optimization
NCM '09 Proceedings of the 2009 Fifth International Joint Conference on INC, IMS and IDC
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
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The Cutting Stock Problem (CSP) is an integer combinatorial optimisation problem (an NP hard problem). It is an important problem in many industrial applications. In recent years, various traditional algorithms have been applied to the CSP, such as the Linear Programming (LP), the Branch and Cut (BC), the Evolutionary Algorithm (EA), etc. To continue improve performance, this paper proposes a novel Shadow Price based Genetic Algorithm (SPGA) to solve the CSP. The main contribution of this work is to combine distinct methods to generate better solutions. The experimental results have shown that the new SPGA has produced much better solutions than the classic Genetic Algorithm (GA) and other bio-inspired algorithms. This paper also demonstrates the new algorithm's capability of solving multi-objective optimisation problems.