Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Introduction to Algorithms
Running time analysis of multiobjective evolutionary algorithms on pseudo-Boolean functions
IEEE Transactions on Evolutionary Computation
Using group selection to evolve leadership in populations of self-replicating digital organisms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Cooperative network construction using digital germlines
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Benefits and drawbacks for the use of epsilon-dominance in evolutionary multi-objective optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Rigorous analyses of fitness-proportional selection for optimizing linear functions
Proceedings of the 10th annual conference on Genetic and evolutionary computation
On the choice of the parent population size*
Evolutionary Computation
Single- and multi-objective evolutionary algorithms for graph bisectioning
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Additive approximations of pareto-optimal sets by evolutionary multi-objective algorithms
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Evolved kernel method for time series
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Proceedings of the 12th annual conference on Genetic and evolutionary computation
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Drift analysis with tail bounds
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
On the effect of populations in evolutionary multi-objective optimisation**
Evolutionary Computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Non-existence of linear universal drift functions
Theoretical Computer Science
Advances in evolutionary multi-objective optimization
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
Runtime analysis of the (1+1) EA on computing unique input output sequences
Information Sciences: an International Journal
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Multi-objective evolutionary algorithms (MOEAs) have become increasingly popular as multi-objective problem solving techniques. An important open problem is to understand the role of populations in MOEAs. We present a simple bi-objective problem which emphasizes when populations are needed. Rigorous runtime analysis point out an exponential runtime gap between the population-based algorithm Simple Evolutionary Multi-objective Optimizer (SEMO) and several single individual-based algorithms on this problem. This means that among the algorithms considered, only the population-based MOEA is successful and all other algorithms fail.