Moreover, his work addressed some innovative issues for search algorithms, like the use of co-evolution. However, he hasn't been able to achieve the best known upper bound and discovered only a 61-comparator solution, presented below. Moreover, Hillis introduced an important bias in his search algorithm by initializing the population with the first 32 comparators of the best known construction. The search was limited around this local optimum and, thus, the final solution still used these initial 32 comparators. The END algorithm can be seen as a massively parallel search algorithm for which elements of the population interact locally.
Simulated evolution is used as the paradigm that focus the search in the state space.
This approach provided impressive results, in particular for the sorting network problem. Compared to Hillis' work, shorter sorting networks were discovered without any initial condition. The paper presenting the END algorithm can be downloaded by clicking here . Some of these sorting networks discovered by END are presented below. Co-evolving parasites improve simulated evolution as an optimization procedure.