Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
How to analyse evolutionary algorithms
Theoretical Computer Science - Natural computing
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Fitness Landscapes Based on Sorting and Shortest Paths Problems
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
The Advantages of Evolutionary Computation
Biocomputing and emergent computation: Proceedings of BCEC97
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Running time analysis of multiobjective evolutionary algorithms on pseudo-Boolean functions
IEEE Transactions on Evolutionary Computation
Analysis of a multiobjective evolutionary algorithm on the 0-1 knapsack problem
Theoretical Computer Science
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
Objective reduction in evolutionary multiobjective optimization: Theory and applications
Evolutionary Computation
Labeling algorithms for multiple objective integer knapsack problems
Computers and Operations Research
On the effects of adding objectives to plateau functions
IEEE Transactions on Evolutionary Computation
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
On the effect of populations in evolutionary multi-objective optimisation**
Evolutionary Computation
Running time analysis of a multiobjective evolutionary algorithm on simple and hard problems
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
On the effect of connectedness for biobjective multiple and long path problems
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
On local search for bi-objective knapsack problems
Evolutionary Computation
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In this paper, the expected running time of two multiobjectiveevolutionary algorithms, SEMO and FEMO, is analyzed for a simpleinstance of the multiobjective 0/1 knapsack problem. The considered problem instance has two profit values per item andcannot be solved by one-bit mutations. In the analysis, we make use of two general upper bound techniques, thedecision space partition method and the graph search method. The paperdemonstrates how these methods, which have previously only beenapplied to algorithms with one-bit mutations, are equally applicablefor mutation operators where each bit is flipped independently with acertain probability.