Parallel Evolutionary Approach of Compaction Problem Using MapReduce
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- Logofătu D., Dumitrescu D. (2010) Parallel Evolutionary Approach of Compaction Problem Using MapReduce. In: Schaefer R., Cotta C., Kołodziej J., Rudolph G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6239. Springer, Berlin, Heidelberg
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. The project, containing the Hadoop implementation can be found at: http://sourceforge.net/projects/dcpsolver/ .
KeywordsData Compaction Static Test Parallel Algorithm Evolution Strategies Greedy Discrete Optimization Apache Hadoop MapReduce Statistical Tests
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