Using GA–ANN algorithm to optimize soft magnetic properties of nanocrystalline mechanically alloyed Fe–Si powders

Freshness measurement of eggs using near infrared (NIR) spectroscopy and multivariate data analysis

“…The squared error of hidden codes of back propagation can be used as objective function of optimizing weight (Yazdanmehr, Anijdan, & Bahrami, 2009). In this way, the problem was transformed into finding the fittest weight set for minimizing objective function.…”

Optimization of manufacturing process effects on brake friction material wear

“…It was trying to find out the set of optimum According to Figure 4, GA start with initial population of random chosen individuals (chromosomes) from the design space and search the input range effectively for required output variables by means of reproduction, crossover, and mutation. [21][22][23][24][25][26][27] In every evolutionary step, known as a generation, the individuals in the current population are decoded (evaluated) according to some predefined quality criterion, referred to as fitness function, i.e. objective function.…”

Composite materials – modelling, prediction and optimization

“…This model was then integrated with a genetic algorithm to provide the optimization model. In accordance with Figure 5.105 , the genetic algorithm started with an initial population of randomly generated individuals (chromosomes) belonging to the design space and searched for the best input values for Schematic illustration of the neural model used for optimization of manufacturing parameters [222] Figure 5.104 the wear model by means of reproduction, crossover and mutation [224][225][226][227][228][229][230]. In every evolutionary step, known as a generation, the individuals in the current population were decoded (evaluated) according to a predefi ned quality criterion, referred to as the fi tness or objective function.…”