Genetic algorithms and grammatical inference

Pandey, Hari Mohan

doi:10.1016/b978-0-12-822116-7.00006-9

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Instead, further iterations tended to lead to biased iteration results, as shown in the 1000th generation. This has been mentioned in many studies [25,26], and this study suggests that it is possible that a flawed elite selection procedure led to sampling errors that affected the iterative robustness [27]. For this study only, the optimization results of the 750th generation can be used for subsequent simulations, which possess more desirable convergence.…”

Section: Optimal Mechanismmentioning

confidence: 99%

Study on the Skeleton Mechanism of Second-Generation Biofuels Derived from Platform Molecules

Fan

Du²,

Liu

et al. 2023

Processes

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This paper focuses on the combustion mechanism of furan-based fuels synthesized from lignocellulose. The fuel is a binary alternative fuel consisting of 2-methylfuran and 2,5-dimethylfuran derived from furfural. The key reactions affecting the combustion mechanism of this fuel were identified via path analysis, and the initial reaction kinetic mechanism was constructed using a decoupling methodology. Then, a genetic algorithm was used to optimize the initial mechanism. The final skeleton mechanism consisted of 67 species and 228 reactions. By comparing experimental data on ignition delay, component concentration, and laminar flame velocity under a wide range of conditions over various fundamental reactors, it was shown that the mechanism has the ability to predict the combustion process of this fuel well.

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Section: Optimal Mechanismmentioning

confidence: 99%

Study on the Skeleton Mechanism of Second-Generation Biofuels Derived from Platform Molecules

Fan

Du²,

Liu

et al. 2023

Processes

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“…GA is designed to explore a wide range of the search space. However, it converges slowly [69,70]. In the proposed GA-CSA hybrid, GA is employed in the CSA initialization phase to generate the crows' initial positions.…”

Section: Hybrid Ga-csamentioning

confidence: 99%

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Roeva,

Zoteva

2023

Fermentation

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Cultivation process (CP) modeling and optimization are ambitious tasks due to the nonlinear nature of the models and interdependent parameters. The identification procedures for such models are challenging. Metaheuristic algorithms exhibit promising performance for such complex problems since a near-optimal solution can be found in an acceptable time. The present research explores a new hybrid metaheuristic algorithm built upon the good exploration of the genetic algorithm (GA) and the exploitation of the crow search algorithm (CSA). The efficiency of the proposed GA-CSA hybrid is studied with the model parameter identification procedure of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation process. The results are compared with those of the pure GA and pure CSA applied to the same problem. A comparison with two deterministic algorithms, i.e., sequential quadratic programming (SQP) and the Quasi-Newton (Q-N) method, is also provided. A more accurate model is obtained by the GA-CSA hybrid with fewer computational resources. Although SQP and Q-N find a solution for a smaller number of function evaluations, the resulting models are not as accurate as the models generated by the three metaheuristic algorithms. The InterCriteria analysis, a mathematical approach to revealing certain relations between given criteria, and a series of statistical tests are employed to prove that there is a statistically significant difference between the results of the three stochastic algorithms. The obtained mathematical models are then successfully verified with a different set of experimental data, in which, again, the closest one is the GA-CSA model. The GA-CSA hybrid proposed in this paper is proven to be successful in the collaborative hybridization of GA and CSA with outstanding performance.

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Genetic algorithms and grammatical inference

Cited by 2 publications

References 9 publications

Study on the Skeleton Mechanism of Second-Generation Biofuels Derived from Platform Molecules

Study on the Skeleton Mechanism of Second-Generation Biofuels Derived from Platform Molecules

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

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