Benjamin Doerr
Tobias Friedrich
ABSTRACT
Island models in evolutionary computation solve problems by a careful interplay of independently running evolutionary algorithms on the island and an exchange of good solutions between the islands. In this work, we conduct rigorous run time analyses for such island models trying to simultaneously obtain good run times and low communication effort.
We improve the existing upper bounds for the communication effort (i) by improving the run time bounds via a careful analysis, (ii) by setting the balance between individual computation and communication in a more appropriate manner, and (iii) by replacing the usual communicate-with-all-neighbors approach with randomized rumor spreading, where each island contacts a randomly chosen neighbor. This epidemic communication paradigm is known to lead to very fast and robust information dissemination in many applications. Our results concern islands running simple (1+1) evolutionary algorithms, we regard d-dimensional tori and complete graphs as communication topologies, and optimize the classic test functions OneMax and LeadingOnes.
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- E. Alba. Parallel evolutionary algorithms can achieve super-linear performance. Information Processing Letters, 82:7--13, 2002.Google Scholar
Cross Ref
- E. Alba, G. Luque, and S. Nesmachnow. Parallel metaheuristies: Recent advances and new trends. International Transactions in Operational Research, 20:1--48, 2013.Google ScholarCross Ref
- G. Badkobeh, P. K. Lehre, and D. Sudholt. Black-box complexity of parallel search with distributed populations. In Proc. of FOGA'15, pages 3--15, 2015. Google ScholarDigital Library
- E. Cantú-Paz. A survey of parallel genetic algorithms. IlliGAL Report, 97003, 1997.Google Scholar
- B. Doerr. Optimal parameter settings for the (1+(λ, λ)) genetic algorithm. CoRR, abs/1604.01088, 2016. Google ScholarDigital Library
- B. Doerr and M. Fouz. Asymptotically optimal randomized rumor spreading. In Proc. of ICALP'11, pages 502--513, 2011. Google ScholarDigital Library
- B. Doerr, D. Johannsen, and C. Winzen. Multiplicative drift analysis. Algorithmica, 64:673--697, 2012. Google ScholarDigital Library
- B. Doerr and M. Künnemann. Tight analysis of randomized rumor spreading in complete graphs. In Proc. of ANALCO'14, pages 82--91, 2014. Google ScholarDigital Library
- B. Doerr and M. Künnemann. Optimizing linear functions with the (1 + λ) evolutionary algorithm - different asymptotic runtimes for different instances. Theoretical Computer Science, 561:3--23, 2015. Google ScholarDigital Library
- S. Droste, T. Jansen, and I. Wegener. On the analysis of the (1 + 1) evolutionary algorithm. Theoretical Computer Science, 276:51--81, 2002. Google ScholarDigital Library
- C. Gießen and C. Witt. Optimal mutation rates for the (1+λ) EA on onemax. In Proc. of GECCO'16, pages 1147--1154, 2016. Google ScholarDigital Library
- J. He and X. Yao. A study of drift analysis for estimating computation time of evolutionary algorithms. Natural Computing, 3(1):21--35, 2004. Google ScholarDigital Library
- D. Johannsen. Random Combinatorial Structures and Randomized Search Heuristics. PhD thesis, Universität des Saarlandes, 2010.Google Scholar
- J. Lässig and D. Sudholt. General upper bounds on the runtime of parallel evolutionary algorithms. Evolutionary Computation, 22:405--437, 2014. Google ScholarDigital Library
- P. K. Lehre and C. Witt. Black-Box Search by Unbiased Variation. Algorithmica, 64:623--642, 2012. Google ScholarDigital Library
- A. Lissovoi and C. Witt. On the utility of island models in dynamic optimization. In Proc. of GECCO'15, pages 1447--1454, 2015. Google ScholarDigital Library
- A. Mambrini and D. Sudholt. Design and analysis of schemes for adapting migration intervals in parallel evolutionary algorithms. Evolutionary Computation, 23:559--582, 2015. Google ScholarDigital Library
- F. Neumann, P. S. Oliveto, G. Rudolph, and D. Sudholt. On the effectiveness of crossover for migration in parallel evolutionary algorithms. In Proc. of GECCO'11, pages 1587--1594, 2011. Google ScholarDigital Library
- M. Ruciński, D. Izzo, and F. Biscani. On the impact of the migration topology on the island model. Parallel Computing, 36:555--571, 2010. Google ScholarDigital Library
- D. Sudholt. Parallel evolutionary algorithms. In Springer Handbook of Computational Intelligence, pages 929--959. 2015.Google ScholarCross Ref
- I. Wegener. Theoretical aspects of evolutionary algorithms. In Proc. of ICALP'01, pages 64--78, 2001. Google ScholarDigital Library
- C. Witt. Optimizing linear functions with randomized search heuristics - the robustness of mutation. In Proc. of STACS'12, pages 420--431, 2012.Google Scholar
- C. Witt. Tight bounds on the optimization time of a randomized search heuristic on linear functions. Combinatorics, Probability and Computing, 22:294--318, 2013.Google ScholarCross Ref
Index Terms
- Island models meet rumor spreading
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