Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
An introduction to genetic algorithms
An introduction to genetic algorithms
The zero/one multiple knapsack problem and genetic algorithms
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Exploring A Two-market Genetic Algorithm
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Evolutionary Game Algorithm for Multiple Knapsack Problem
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
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ExGA I, a previously presented genetic algorithm, successfully solved numerous instances of the multiple knapsack problem (MKS) by employing an adaptive repair function that made use of the algorithm's modular encoding. Here we present ExGA II, an extension of ExGA I that implements additional features which allow the algorithm to perform more reliably across a larger set of benchmark problems. In addition to some basic modifications of the algorithm's framework, more specific extensions include the use of a biased mutation operator and adaptive control sequences which are used to guide the repair procedure. The success rate of ExGA II is superior to its predecessor, and other algorithms in the literature, without an overall increase in the number of function evaluations required to reach the global optimum. In fact, the new algorithm exhibits a significant reduction in the number of function evaluations required for the largest problems investigated. We also address the computational cost of using a repair function and show that the algorithm remains highly competitive when this cost is accounted for.