this study is focused on the development of a systematic computational approach which implements genetic algorithm (ga) to find the optimal rigorous kinetic models.a general kinetic model for hydrogenolysis of dibenzothiophene (dbt) based on langmuir-hinshelwood type has been obtained from open literature. this model consists of eight continuous parameters(e.g., arrhenus  and van't hoff paramet...

Numerical Control, NC, machine tool feed servo systems are time-varying, but with the poor anti-interference of conventional PID control it is difficult to achieve satisfactory setting effect. Thus this paper puts forward an online adaptive fuzzy PID setting strategy. Further, in order to improve the control performance of fuzzy PID controllers, we use a genetic algorithm to set the initial PID...

A near-optimal midcourse trajectory shaping guidance algorithm is proposed for both air and ballistic target engagement mission attributes for generic long range interceptor missile. This guidance methodology is based on the maximum final velocity as the objective function and maximum permissible flight altitude as the in-flight state constraint as well as the head-on orientation as the termina...

This article has demonstrated chaos Control in the Lorenz using the backstepping approach. Backstepping method has positive parameters. mostly, parameters are selected arbitrarily , the values of these parameters are significant because the incorrect values lead to instability. Genetic algorithm finds best values for parameters. GA by minimizing the fitness function can find the optimal values ...

Random Early Detection (RED) is one of the most commonly used Active Queue Management (AQM) algorithms that is recommended by IETF for deployment in the network. Although RED provides low average queuing delay and high throughput at the same time, but effectiveness of RED is highly sensitive to the RED parameters setting. As network condition varies largely, setting RED's parameters with fixed ...

Genetic algorithms, and other forms of evolutionary computation, are controlled by numerous parameters, such as crossover and mutation rates, population size, among others depending upon the specific form of evolutionary computation as well as which operators are employed. Setting the values for these parameters is no simple task. In this paper, we develop a genetic algorithm with adaptive cont...

running a genetic algorithm entails setting a number of parameter values. Finding settings that work well on one problem is not a trivial task and a genetic algorithm performance can be severely impacted. Moreover we know that in natural environments population sizes, reproduction and competition rates, change and tend to stabilise around appropriate values according to some environmental facto...