Feature selection is an essential process for machine learning tasks since it improves generalization capabilities, and reduces run-time and amodel’s complexity. Inmany applications, the cost of collecting the features must be taken into account. To cope with the cost problem, we developed a new cost-sensitive fitness function based on histogram comparison. This function is integrated with a ge...

Abstract Background and Purpose: Studies carried out in recent decades show that exercises have useful impact on improving executive functions. Therefore, this study aims to investigate the roles of fitness and aerobic exercises on problem-solving process of dyslexic and dyscalculia children. Materials and methods: Using systematic review study and key terms including fitness and aerobic e...

In this paper, we present a genetic algorithm (GA) for optimization of a multi-mode resource constrained time cost trade off (MRCTCT) problem. The proposed GA, each activity has several operational modes and each mode identifies a possible executive time and cost of the activity. Beyond earlier studies on time-cost trade-off problem, in MRCTCT problem, resource requirements of each execution mo...

This paper proposes a modified Genetic Programming method for forecasting the mobile telecommunications subscribers’ population.Themethod constitutes an expansion of the hybridGenetic Programming (hGP)method improved by the introduction of diffusion models for technological forecasting purposes in the initial population, such as the Logistic, Gompertz, and Bass, as well as the Bi-Logistic and L...

This thesis introduces Intelligent Fitness Functions and Partial Fitness Functions both of which can improve the performance of a genetic algorithm which is limited to a fixed run time. An Intelligent Fitness Function is defined as a fitness function with a memory. The memory is used to store information about individuals so that duplicate individuals do not need to have their fitness tested. D...

Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...