Solving Multidimensional Knapsack Problem with Bayesian Multiploid Genetic Algorithm

Year 2022, , 58 - 64, 28.12.2022

Emrullah Gazioğlu

https://doi.org/10.55195/jscai.1216193

Cited By: 1

Abstract

Solving optimization problems is still a big challenge in the area of optimization algorithms. Many proposed algorithms in the literature don’t consider the relations between the variables of the nature of the problem. However, a recently published algorithm, called “Bayesian Multiploid Genetic Algorithm” exploits the relations between the variables and then solves the given problem. It also uses more than one genotype unlike the simple Genetic Algorithm (GA) and it acts like an implicit memory in order to remember the old but good solutions. In this work, the well-known Multidimensional Knapsack Problem (MKP) is solved by the Bayesian Multiploid Genetic Algorithm. And the results show that exploiting relations between the variables gets a huge advantage in solving the given problem.

Keywords

genetic algorithm, evolutionary computing, multidimensional knapsack problem

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