Non-cryptographic (NC) hash functions are the core part of many networking and security applications such as traffic flow monitoring and deep packet inspection. For these applications, speed is more important than strong cryptographic properties. In Terabit Ethernet networks, the speed of the hash functions can have a significant impact on the overall performance of the system when it is required to process the packets at a line rate. Hence, improving the speed of hash functions can have a significant impact on the overall performance of such architectures. Designing a good hash function is a challenging task because of the highly non-linear and complex relationship between input and output variables. Techniques based on Evolutionary Computation (EC) excel in addressing such challenges. In this paper, we propose novel fast non-cryptographic hash functions using genetic programming, and we call the resulting hash functions the GPNCH (Genetic Programming-based Non-Cryptographic Hash) family. We choose to employ avalanche metrics as a fitness function because the networking and security applications we consider require hash functions to be uniform and independent. We evaluate the performance of GPNCH functions on FPGA and compare the delay, throughput, and resource occupation with the state-of-the-art NC hash functions that satisfy the avalanche criteria. We show that GPNCH functions outperform the other algorithms in terms of latency, operating frequency, and throughput at the modest cost of hardware resources.