Network application logs (squid logs, ftp logs, mail logs etc.) are a paramount source of network information. By a careful analysis of these logs, a network performance metric (throughput etc.) can be obtained. Since these records contain past information, any conventional analysis would result in network performance indices which are somehow static. On the other hand, network resources as well as the utilities are dynamic. It thus poses a difficult task to understand how the network would behave at some time in future. We present a estimation model of the network behavior dynamics exploiting its past activities. The approached model employs a sophisticated application of standard statistical estimation techniques applied in addition to a variant of genetic algorithm operators. This involves introducing new statistical operators to adapt to their genetic algorithm counterparts. We have carried out experiments using a one year long network application log archive, presented the results and displayed the evaluation of our approach.