Experimental analysis of a statistical multiploid genetic algorithm for dynamic environments

Dynamic environments are still a big challenge for optimization algorithms. In this paper, a Genetic Algorithm using both Multiploid representation and the Bayesian Decision method is proposed. By Multiploid representation, an implicit memory scheme is introduced to transfer useful information to the next generations. In this representation, there are more than one genotypes and only one phenotype. The phenotype values are determined based on the corresponding genotypes values. To determine phe-notype values, the well-known Bayesian Optimization Algorithm (BOA) has been injected into our algo-rithm to create a Bayes Network by using the previous population to exploit interactions between variables. With this algorithm, we have solved the well-known Dynamic Knapsack Problem (DKP) with 100, 250, and 500 items. Also, we have compared our algorithm with the most recent algorithm in the literature by using the DKP with 100 items. Experiments have shown that the proposed algorithm is effi-cient and faster than the peer algorithms in the manner of tracking moving optima without using an explicit memory scheme. In conclusion, using relationships between variables within the optimization algorithms is useful when concerning dynamic environments.(c) 2022 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This paper proposes a Genetic Algorithm using both Multiploid representation and the Bayesian Decision method to tackle dynamic environments. By injecting the Bayesian Optimization Algorithm into the algorithm, a Bayes Network is created to exploit interactions between variables, resulting in efficient and faster solution for Dynamic Knapsack Problem.