This study introduces, two models based on Gene Expression Programming (GEP) to predict compressive strength of high strength concrete (HSC). Composition of HSC was assumed simplified, as a mixture of six components (cement, silica fume, super-plastisizer, water, fine aggregate and coarse aggregate). The 28-day compressive strength value was considered the target of the prediction.  Data on 159...

Gene expression programming (GEP) uses mutation, transposition , and crossover to create variation. Although there exists a large body of work in genetic algorithms concerning the roles of mutation and recombination, these results not only do not apply to GEP due to the genotype/ phenotype representation but also seem to contradict the GEP experience. Therefore, and given the diversity of GEP o...

This paper introduces GEP(Gene Expression Programming) fundamental. Aimed at prediction of software failure sequence, an improved GEP(GEP based on Block Strategy, BS-GEP) is presented, in which the population is divided into several blocks according to the individual fitness of each generation and the genetic operators are reset differently in each block to guarantee the genetic diversity. The ...

In this research, different amounts of nano-MgO were added to normal concrete samples, and the effect these particles on durability samples under freeze thaw conditions was investigated. The compressive tensile strength as well permeability containing nanoparticles measured compared those plain (without nanoparticles). age percentage nanoparticles, water-to-binder ratio are variables current re...

Gene Expression Programming (GEP) is a novel genetic algorithm, a highly effective, stable random searching method. We take GEP to make models of Quantitative Structure-Retention Relationship (QSRR) for a series of oxygen-containing organic compounds of GC retention index, and compare the predictive results with Artificial Neural Network (ANN) and Multiple Linear Regression (MLR). The correlati...

Most of the potential function expressions subjected to function finding contain constant terms. Gene Expression Programming (GEP)’s effectiveness of function finding has been severely limited due to a lack of constant creating ability. Though this issue has been realized and discussed by many GEP researchers, an efficient method has never been established. This paper presents a new method to f...

In the study, we present a higher effective algorithm, called revised gene expression programming (RGEP), to construct the model for music emotion recognition. Our main contributions are as follows: firstly, we describe the basic mechanisms of music emotion recognition and introduce gene expression programming (GEP) to deal with the model construction for music emotion recognition. Secondly, we...