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
Fitness Landscapes Based on Sorting and Shortest Paths Problems
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Reducing Local Optima in Single-Objective Problems by Multi-objectivization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Running time analysis of evolutionary algorithmson a simplified multiobjective knapsack problem
Natural Computing: an international journal
On the Choice of the Offspring Population Size in Evolutionary Algorithms
Evolutionary Computation
Runtime Analysis of the (μ+1) EA on Simple Pseudo-Boolean Functions
Evolutionary Computation
On the effect of populations in evolutionary multi-objective optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Minimum spanning trees made easier via multi-objective optimization
Natural Computing: an international journal
Analysis of a multiobjective evolutionary algorithm on the 0-1 knapsack problem
Theoretical Computer Science
Approximating covering problems by randomized search heuristics using multi-objective models
Proceedings of the 9th 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
Population size versus runtime of a simple evolutionary algorithm
Theoretical Computer Science
Approximating Minimum Multicuts by Evolutionary Multi-objective Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Analyzing Hypervolume Indicator Based Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
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
On the impact of the mutation-selection balance on the runtime of evolutionary algorithms
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
Analysis of a simple evolutionary algorithm for the multiobjective shortest path problem
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Evolutionary algorithms and dynamic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
On the effects of adding objectives to plateau functions
IEEE Transactions on Evolutionary Computation
Convergence time analysis for the multi-objective counting ones problem
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Running time analysis of multiobjective evolutionary algorithms on pseudo-Boolean functions
IEEE Transactions on Evolutionary Computation
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Computational complexity analysis of multi-objective genetic programming
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Convergence of set-based multi-objective optimization, indicators and deteriorative cycles
Theoretical Computer Science
Parsimony pressure versus multi-objective optimization for variable length representations
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Population size matters: rigorous runtime results for maximizing the hypervolume indicator
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Information Sciences: an International Journal
| Hi-index | 0.01 |
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 two simple bi-objective problems which emphasise when populations are needed. Rigorous runtime analysis points out an exponential runtime gap between the population-based algorithm simple evolutionary multi-objective optimiser (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.