In this paper, a new hybrid of genetic algorithm (GA) and simulated annealing (SA), referred to as GSA, is presented. In this algorithm, SA is incorporated into GA to escape from local optima. The concept of hierarchical parallel GA is employed to parallelize GSA for the optimization of multimodal functions. In addition, multi-niche crowding is used to maintain the diversity in the population o...

Heart disease is a leading cause of death worldwide, and the need for effective predictive systems major source to treat affected patients. This study aimed determine how improve accuracy Random Forest in predicting classifying heart disease. The experiments performed this were designed select most optimal parameters using an RF optimization technique GA. Genetic Algorithm (GA) used optimize pr...

quantitative relationships between molecular structure and azolo-adamantanes derivatives were discovered by different chemometric tools including factor analysis based multiple linear regressions (fa-mlr), principle component regression analysis (pcra), and genetic algorithm-partial least squares ga-pls. the fa-mlr describes the effect of geometrical and quantum indices on enzyme inhibition act...

in this study, a hybrid intelligent model has been designed to predict groundwater inflow to a mine pit during its advance. novel hybrid method coupling artificial neural network (ann) with genetic algorithm (ga) called ann-ga, was utilised. ratios of pit depth to aquifer thickness, pit bottom radius to its top radius, inverse of pit advance time and the hydraulic head (hh) in the observation w...

Genetic algorithms (GAs) are biologically motivated adaptive systems which have been used, with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within the framework of competitive learning. This new perspective reveals a number of different possibili...

This paper has been worked on a RAP with multi-state components and the performance rate of each component working state may increase by spending technical and organizational activities costs. Whereas RAP belongs to Np-Hard problems, we used Genetic algorithm (GA) and simulated annealing (SA) and for solving the presented problem and calculating system reliability universal generating function ...

In this paper, we discuss on new Coevolutionary Genetic Algorithm for Constraint Satisfaction. Our basic idea is to explore effective genetic information in the population, i.e., schemata, and to exploit the genetic information in order to guide the population to better solutions. Our Coevolutiona,ry Genetic Algorithm (CGA) consists of two GA populations; the first GA, called ”H-GA”, searches f...

Despite a large number of advantages, Torque Ripple (TR) is the most important drawback of Switched Reluctance Motor (SRM). In the presented study, TR was reduced by optimizing the gate pulse angle of the SRM phase which played a leading role in the generated torque profile. For the Optimization, one of the strategies of Genetic Algorithm (GA) which was named Non-dominated Sorting Genetic Algor...

in this paper, a connecting rod was modeled and analyzed by finite element method. byusing genetic algorithm (ga) and modified cuckoo optimization algorithm (mcoa), the materialproperties and some geometrical specifications were optimized. cost function was a combination ofweight and stress. the connecting rod was under a load of 21.8 kn which obtained 360o crank angleat 5700 rev/min. the reduc...

Yard crane is an important resource in container terminals. Efficient utilization of the yard crane significantly improves the productivity and the profitability of the container terminal. This paper presents a mixed integer programming model for the yard crane scheduling problem with non- interference constraint that is NPHARD in nature. In other words, one of the most important constraints in...