Knapsack Problem Solving Using Evolutionary Algorithms Guided by Complex Networks
The knapsack problem is a well known optimization problem solved using evolutionary computation. In this work we present that the population of the evolutionary computation can be represented as complex networks, as follows: a graph where nodes represents individual solutions and the links represent the crosses among solutions. Furthermore, to improve solutions, new links among nodes of the population are created, thus, the algorithm is guided and the population dynamics of the evolutionary algorithm to solve the knapsack problem. We evaluate that one strategy guided by a complex dynamic network yields better results than a traditional algorithm. The results show that the model proposed improves convergence compared to the traditional solution and shorter execution times. Strategies based on small-world networks are promising in the experiments compared to those using the traditional evolutionary strategy.
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