Combinatorial optimization by stochastic evolution

A novel technique is introduced, called stochastic evolution (SE), for solving a wide range of combinatorial optimization problems. It is shown that SE can be specifically tailored to solve the network bisection, traveling salesman, and standard cell placement problems. Experimental results for these problems show that SE can produce better quality solutions than sophisticated simulated annealing (SA)-based heuristics in a much shorter computation time.<
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