DHE\(^{2}\): Distributed Hybrid Evolution Engine for Performance Optimizations of Computationally Intensive Applications

A large number of real-world optimization and search problems are too computationally intensive to be solved due to their large state space. Therefore, a mechanism for generating approximate solutions must be adopted. Genetic Algorithms, a subclass of Evolutionary Algorithms, represent one of the widely used methods of finding and approximating useful solutions to hard problems. Due to their population-based logic and iterative behaviour, Evolutionary Algorithms are very well suited for parallelization and distribution. Several distributed models have been proposed to meet the challenges of implementing parallel Evolutionary Algorithms. Among them, the MapReduce paradigm proved to be a proper abstraction of mapping the evolutionary process. In this paper, we propose a generic framework, i.e., DHE\(^{2}\) (Distributed Hybrid Evolution Engine), that implements distributed Evolutionary Algorithms on top of the MapReduce open-source implementation in Apache Hadoop. Within DHE\(^{2}\), we propose and implement two distributed hybrid evolution models, i.e., the MasterSlaveIslands and MicroMacroIslands models, alongside a real-world application that avoids the local optimum for clustering in an efficient and performant way. The experiments for the proposed application are used to demonstrate DHE\(^{2}\) increased performance.