generally composed of coded genotype strings, statistically definedcontrol parameters, a fitness function, genetic operations (reproduction, crossover and mutation), and mechanisms for selection and encoding of
Fig. 6. Flowchart for Genetic Algorithm. 4.1.4 GA and its variants for parameter estimation The idea of using Genetic Algorithm method was first applied to optimize a PEMFC stack design by finding the best configuration in terms of number of series, parallel cells and cell surface area in [...
The integration between the Artificial Intelligent (AI) models (including the RBF) and the Genetic Algorithm (GA) has been developed in several prediction/forecasting engineering applications. In these existing models, the GA “as optimizer” has been integrated with the AI model “as predictor” ...
Flowchart describing the genetic algorithm for ANN hyperparameter optimization Full size image 2.3 Model predictive control Model predictive control (MPC) consists of three parts: the cost function, the optimizer, and the system model. In this paper, the system model is represented by the dual-net...
Diagram - Selection operation without coupling operation flowchart The next step performed by a genetic algorithm is the introduction of new chromosomes into a population. Offspring chromosomes can form a new population and replace the entire [previous] population [non-overlapping population], or they ...
Flowchart of the proposed genetic algorithm optimized hybrid model for agricultural price forecasting. Full size image VMD is instrumental in decomposing complex, nonstationary price series into IMFs, effectively mitigating noise and extracting essential price dynamics. Unlike existing VMD-based models that...
A flowchart summarizing the study design. Blue highlight indicates study data, green indicates a step in the study, orange indicates an exclusion of data and white describes the methodology applied at this step. QC, quality control; CMR, Cardiovascular magnetic resonance; GWAS, genome-wide associat...
What more broadly applicable principles are embodied in these two systems? For example, what fraction of these methodologies can be applied to the problem of getting a robot to mop the floor of an obstacle-laden room? Correctly recognizing images or patterns? Devising an algorithm to solve a ...
In this paper, we propose a Modified Electro Search (HESGA) algorithm. HESGA is a type of meta-heuristic optimization algorithm that works with the notion of the flow of electrons nearby nucleus using the orbits. The proposed method is designed by modifying the Electro Search Algorithm with ...
The genetic algorithm (GA) is an evolutionary method inspired by Darwin’s theory, which can enhance the generalization performance of artificial models [31], [32], [33]. The objective of the present study is to provide a reliable prediction model for disc cutter life by using AI technology...