A Genetic Algorithm for Obtaining Memory Constrained Near-Perfect Hashing
This provides a solution for memory-constrained applications in computer science, such as operating systems and databases, where search time is critical, though it appears incremental.
The paper tackled the problem of fast item retrieval from fixed collections by developing a hash table approach that minimizes comparisons and memory usage, achieving near-perfect hashing that is faster than binary search and uses less memory than perfect hashing.
The problem of fast items retrieval from a fixed collection is often encountered in most computer science areas, from operating system components to databases and user interfaces. We present an approach based on hash tables that focuses on both minimizing the number of comparisons performed during the search and minimizing the total collection size. The standard open-addressing double-hashing approach is improved with a non-linear transformation that can be parametrized in order to ensure a uniform distribution of the data in the hash table. The optimal parameter is determined using a genetic algorithm. The paper results show that near-perfect hashing is faster than binary search, yet uses less memory than perfect hashing, being a good choice for memory-constrained applications where search time is also critical.