Intelligent Decision Systems in Large-Scale Distributed Environments

Volume 362 of the series Studies in Computational Intelligence pp 279-291

Distributed Evolutionary Algorithm Using the MapReduce Paradigm – A Case Study for Data Compaction Problem

  • Doina LogofătuAffiliated withUniversity of Applied Sciences
  • , Manfred GruberAffiliated withUniversity of Applied Sciences
  • , Dumitru Dan DumitrescuAffiliated withBabeş-Bolyai University

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A parallel evolutionary approach of Compaction Problem is introduced using MapReduce. This problem is of interest for VLSI testing and bioinformatics. The overall cost of a VLSI circuit’s testing depends on the length of its test sequence; therefore the reduction of this sequence, keeping the coverage, will lead to a reduction of used resources in the testing process. The problem of finding minimal test sets is NP-hard. We introduce a distributed evolutionary algorithm (MapReduce Parallel Evolutionary Algorithm–MRPEA) and compare it with two greedy approaches. The proposed algorithms are evaluated on randomly generated five-valued benchmarks that are scalable in size. The MapReduce paradigm offers the possibility to distribute and scale large amount of data. Experiments show the efficiency of the proposed parallel approach.


Data Compaction Static Test Parallel Algorithm Evolution Strategies Greedy Discrete Optimization Apache Hadoop MapReduce Statistical Tests