On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Introduction to Algorithms
Global Optimization by Means of Distributed Evolution Strategies
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time (Natural Computing Series)
The influence of migration sizes and intervals on island models
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Runtime Analysis of the (μ+1) EA on Simple Pseudo-Boolean Functions
Evolutionary Computation
Parallel Evolutionary Computations (Studies in Computational Intelligence)
Parallel Evolutionary Computations (Studies in Computational Intelligence)
A building-block royal road where crossover is provably essential
Proceedings of the 9th annual conference on Genetic and evolutionary computation
An analysis of island models in evolutionary computation
An analysis of island models in evolutionary computation
Analysis of diversity-preserving mechanisms for global exploration*
Evolutionary Computation
Analysis of the (1 + 1)-EA for finding approximate solutions to vertex cover problems
IEEE Transactions on Evolutionary Computation
The benefit of migration in parallel evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
General scheme for analyzing running times of parallel evolutionary algorithms
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Approximating covering problems by randomized search heuristics using multi-objective models*
Evolutionary Computation
Adaptive population models for offspring populations and parallel evolutionary algorithms
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
On the analysis of the simple genetic algorithm
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Homogeneous and heterogeneous island models for the set cover problem
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Improved runtime analysis of the simple genetic algorithm
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Island models are popular ways of parallelizing evolutionary algorithms as they can decrease the parallel running time at low communication costs and lead to an increased population diversity. This in particular provides a good setting for crossover as this operator relies on a good diversity between parents. We consider the effect of recombining migrants with individuals on the target island. We rigorously prove, for a test function in pseudo-Boolean optimization, exponential performance gaps between island models with strongly connected topologies and a panmictic (mu+1)-EA as long as the migration interval is not too small. We then choose vertex cover as a classical NP-hard problem. By considering instances with a clear building block structure we prove that, also in this more practical setting, island models with a particular topology drastically outperform panmictic populations. Both the theoretical and empirical results show that for strongly connected topologies, such as ring, the performance drops by decreasing the migration interval, while this is not the case for topologies connected weakly such as the single receiver model.