Computer Science – Neural and Evolutionary Computing
Scientific paper
2011-09-08
Computer Science
Neural and Evolutionary Computing
An extended abstract will appear in the proceedings of the 22nd International Symposium on Algorithms and Computation (ISAAC 2
Scientific paper
Evolutionary algorithms are popular heuristics for solving various combinatorial problems as they are easy to apply and often produce good results. Island models parallelize evolution by using different populations, called islands, which are connected by a graph structure as communication topology. Each island periodically communicates copies of good solutions to neighboring islands in a process called migration. We consider the speedup gained by island models in terms of the parallel running time for problems from combinatorial optimization: sorting (as maximization of sortedness), shortest paths, and Eulerian cycles. Different search operators are considered. The results show in which settings and up to what degree evolutionary algorithms can be parallelized efficiently. Along the way, we also investigate how island models deal with plateaus. In particular, we show that natural settings lead to exponential vs. logarithmic speedups, depending on the frequency of migration.
Lässig Jörg
Sudholt Dirk
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