Genetic Algorithm For Convolutional Neural Network Hyperparameter Tuning

Image classification has been applied in various fields, from agriculture to health. In order to perform well, an image classification method needs an appropriate hyperparameter setting. However, tuning hyperparameters in a classification method is not an easy task. Many approaches have been developed to solve the problem, such as combining metaheuristics methods with a convolutional neural network (CNN). An example of metaheuristics method is genetic algorithm. Genetic algorithms have been proven to optimize machine learning and deep learning. This research contributes to the automatic tuning of hyperparameters usinggenetic algorithms. The proposed method is evaluated using MNIST dataset. The experiments results show that using a genetic algorithm for tuning hyperparameters automatically, the accuracy of validation data is 97.02% and the accuracy of training data is 99.77%. The performance of Genetic Algorithm is compared with Harmony Search. The accuracy of Harmony Search for validation dat is 83.96% and for training data is 88.33%. The Genetic algorithm takes 24,97 seconds for training while Harmony Search needs 56.44 seconds

Parameter Setting with Dynamic Island Models

In this paper we proposed the use of a dynamic island model which aim at adapting parameter settings dynamically. Since each island corresponds to a specific parameter setting, measuring the evolution of islands populations sheds light on the optimal parameter settings efficiency throughout the search. This model can be viewed as an alternative adaptive operator selection technique for classic steady state genetic algorithms. Empirical studies provide competitive results with respect to other methods like automatic tuning tools. Moreover, this model could ease the parallelization of evolutionary algorithms and can be used in a synchronous or asynchronous way

Genetic algorithms applications in load frequency control

This paper deals with the application of genetic algorithms for optimizing the parameters of conventional automatic generation control (AGC) systems. A two-area nonreheat thermal system is considered to exemplify the optimum parameter search. A digital simulation is used in conjunction with the genetic algorithm optimization process. The integral of the square of the error and the integral of time-multiplied absolute value of the error performance indices are considered in the search for the optimal AGC parameters. The results reported in this paper demonstrate the effectiveness of the genetic algorithms in the tuning of the AGC parameter

Genetic algorithms applications in load frequency control

This paper deals with the application of genetic algorithms for optimizing the parameters of conventional automatic generation control (AGC) systems. A two-area nonreheat thermal system is considered to exemplify the optimum parameter search. A digital simulation is used in conjunction with the genetic algorithm optimization process. The integral of the square of the error and the integral of time-multiplied absolute value of the error performance indices are considered in the search for the optimal AGC parameters. The results reported in this paper demonstrate the effectiveness of the genetic algorithms in the tuning of the AGC parameter

Tuning of AGC of interconnected reheat thermal systems with geneticalgorithms

This paper deals with the application of genetic algorithms for optimizing the automatic generation control (AGC) systems. An integral controller and a proportional-plus-integral controller are considered. A two-area reheat thermal system is considered to exemplify the optimum parameter search. A digital simulation is used in conjunction with the genetic algorithm optimization process. The integral of the square of the error and the integral of time-multiplied absolute value of the error performance indices are considered in the search for the optimal AGC parameters. The results reported in this paper demonstrate the effectiveness of the genetic algorithms in the tuning of the AGC parameter

Tuning of AGC of interconnected reheat thermal systems with geneticalgorithms

This paper deals with the application of genetic algorithms for optimizing the automatic generation control (AGC) systems. An integral controller and a proportional-plus-integral controller are considered. A two-area reheat thermal system is considered to exemplify the optimum parameter search. A digital simulation is used in conjunction with the genetic algorithm optimization process. The integral of the square of the error and the integral of time-multiplied absolute value of the error performance indices are considered in the search for the optimal AGC parameters. The results reported in this paper demonstrate the effectiveness of the genetic algorithms in the tuning of the AGC parameter

On-line multiobjective automatic control system generation by evolutionary algorithms

Evolutionary algorithms are applied to the on- line generation of servo-motor control systems. In this paper, the evolving population of controllers is evaluated at run-time via hardware in the loop, rather than on a simulated model. Disturbances are also introduced at run-time in order to pro- duce robust performance. Multiobjective optimisation of both PI and Fuzzy Logic controllers is considered. Finally an on-line implementation of Genetic Programming is presented based around the Simulink standard blockset. The on-line designed controllers are shown to be robust to both system noise and ex- ternal disturbances while still demonstrating excellent steady- state and dvnamic characteristics