reducing internal fragmentation in segregated free lists using genetic algorithms
In this paper we present an approach for improving memory efficiency using genetic algorithms. More precisely, we improve the internal memory fragmentation by finding the optimal configuration of a segregated free lists data structure. We have used trace instrumentation to generate the workload of memory allocations and deallocations from significant scenarios.The genetic algorithm used the workload as input to generate the optimal configuration among the huge number of potential solutions by evolving an initial population (a set of potential solutions). In practice, memory configurations are created on the empirical evidence based on the fact that the system works. However, a more scientific and rational approach is possible by using genetic algorithms. The approach we have used was fast and effective in providing the configuration parameters for the segregated free lists. The result is based on the use of heuristics and provides an excellent choice when a brute force approach is not feasible. Moreover, the use of genetic algorithms shows that the software engineering discipline can benefit from different research areas where complexity, adaptation and evolution are involved.
genetic algorithms optimization dynamic memory performance