Approaches to growth models and new growth models

In order to find the function that best describes living things or phenomena, growth models are compared by means of various criteria, and sometimes new ones are introduced. In this study, two new growth models, namely the Combined Sloboda Gompertz Model and the Generalized Crescenzo & Spina Model, are proposed within the scope of the main purpose. The performances of the models were compared with the growth models on which they were based, and their effectiveness was revealed. In addition, it has been shown that growth models such as Gompertz, Schnute, Logistic-Karkach can be used instead of the von Bertalanffy model, which is widely used in the length-age distribution for the P. quadrilineatus species. The length-frequency distribution of the Lessepsian Crab was determined by ELEFAN and Genetic Algorithm. The length-weight distributions of two different fish species were determined by Artificial Neural Networks and Linear Regression, and the competence of Artificial Neural Networks was shown. The performances of Least Squares and Genetic Algorithm methods in parameter estimation were compared. All methods and models were compared according to criteria such as R^2, Mean Squared Error, Root Mean Squared Error, Mean Absolute Percent Error. Key Words: Combined Sloboda Gompertz Model, Generalized Growth Model, Growth Models, Artificial Neural Networks, Genetic Algorithm