A genetic algorithm for the unbounded knapsack problem
ABSTRACT In this paper a new evolutionary algorithm is presented for the unbounded knapsack problem, which is a famous NP-complete combinatorial optimization problem. The proposed genetic algorithm is based on two techniques. One is a heuristic operator, which utilizes problem-specific knowledge, and the other is a preprocessing technique. Computational results show that the proposed algorithm is capable of obtaining high-quality solutions for problems of standard randomly generated knapsack instances, while requiring only a modest amount of computational effort.
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ABSTRACT: Problem statement: Knapsack problem is a typical NP complete problem. During last few decades, Knapsack problem has been studied through different approaches, according to the theoretical development of combinatorial optimization. Approach: In this study, modified evolutionary algorithm was presented for 0/1 knapsack problem. Results: A new objective_func_evaluation operator was proposed which employed adaptive repair function named as repair and elitism operator to achieve optimal results in place of problem specific knowledge or domain specific operator like penalty operator (which are still being used). Additional features had also been incorporated which allowed the algorithm to perform more consistently on a larger set of problem instances. Conclusion/Recommendations: This study also focused on the change in behavior of outputs generated on varying the crossover and mutation rates. New algorithm exhibited a significant reduction in number of function evaluations required for problems investigated.Journal of Computer Science. 01/2009;
Conference Paper: Solving Unbounded Knapsack Problem Based on Quantum Genetic Algorithms.[Show abstract] [Hide abstract]
ABSTRACT: Resource distribution, capital budgeting, investment decision and transportation question could form as knapsack question models. Knapsack problem is one kind of NP-Complete problem and Unbounded Knapsack problems (UKP) are more complex and harder than general Knapsack problems. In this paper, we apply QGAs (Quantum Genetic Algorithms) to solve Unbounded Knapsack Problem. First, present the problem into the mode of QGAs and figure out the corresponding genes types and their fitness functions. Then, find the perfect combination of limitation and largest benefit. Finally, quant bit is updated by adjusting rotation angle and the best solution is found. In addition, we use the strategy of greedy method to arrange the sequence of chromosomes to enhance the effect of executing. Preliminary experiments prove our system is effective. The system also compare with AGAs (Adaptive Genetic Algorithms) to estimate their performances.Intelligent Information and Database Systems, Second International Conference, ACIIDS, Hue City, Vietnam, March 24-26, 2010. Proceedings, Part I; 01/2010
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ABSTRACT: Knapsack problem is regarded as a difficult NP problem in computer algorithms. According to the characteristics of knapsack problems, an algorithm (called KP-KEA) for solving knapsack problems utilizing knowledge evolution principle is proposed. In this algorithm, an initial knowledge base is formed at first. The next work is to inherit excellent knowledge individuals by inheritance operator, produce novel knowledge individuals by innovation operator, update knowledge-base by update operator, and accordingly realize knowledge evolution. At last, the optimal solution of knapsack problems can be gained from the optimal knowledge individual. The successful experimental results show that it is an effective algorithm for solving knapsack problems. Compared with genetic algorithm, the convergence speed and the optimal solution of this algorithm are all better. This algorithm is also suited to solve other constraint optimization problems.01/2011;