Do additional objectives make a problem harder?
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Approximating covering problems by randomized search heuristics using multi-objective models
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Analysis of evolutionary algorithms for the longest common subsequence problem
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
Computing minimum cuts by randomized search heuristics
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Simulated annealing, its parameter settings and the longest common subsequence problem
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Multiobjectivization by Decomposition of Scalar Cost Functions
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Approximating Minimum Multicuts by Evolutionary Multi-objective Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Runtime Analyses for Using Fairness in Evolutionary Multi-Objective Optimization
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Investigations into the Effect of Multiobjectivization in Protein Structure Prediction
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Ant Colony Optimization and the Minimum Spanning Tree Problem
Learning and Intelligent Optimization
On the size of weights in randomized search heuristics
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
Objective reduction in evolutionary multiobjective optimization: Theory and applications
Evolutionary Computation
On the effects of adding objectives to plateau functions
IEEE Transactions on Evolutionary Computation
A pheromone-rate-based analysis on the convergence time of ACO algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Runtime analysis of a binary particle swarm optimizer
Theoretical Computer Science
Ant Colony Optimization and the minimum spanning tree problem
Theoretical Computer Science
Approximating covering problems by randomized search heuristics using multi-objective models*
Evolutionary Computation
On the effect of populations in evolutionary multi-objective optimisation**
Evolutionary Computation
Exploring the runtime of an evolutionary algorithm for the multi-objective shortest path problem**
Evolutionary Computation
Improvement of bounded-diameter MST instances with hybridization of multi-objective EA
Proceedings of the 2011 International Conference on Communication, Computing & Security
Illustration of fairness in evolutionary multi-objective optimization
Theoretical Computer Science
An analysis on recombination in multi-objective evolutionary optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Multimodal optimization using a bi-objective evolutionary algorithm
Evolutionary Computation
GECCO 2012 tutorial on evolutionary multiobjective optimization
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
GECCO 2013 tutorial on evolutionary multiobjective optimization
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
An analysis on recombination in multi-objective evolutionary optimization
Artificial Intelligence
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Many real-world problems are multi-objective optimization problems and evolutionary algorithms are quite successful on such problems. Since the task is to compute or approximate the Pareto front, multi-objective optimization problems are considered as more difficult than single-objective problems. One should not forget that the fitness vector with respect to more than one objective contains more information that in principle can direct the search of evolutionary algorithms. Therefore, it is possible that a single-objective problem can be solved more efficiently via a generalized multi-objective model of the problem. That this is indeed the case is proved by investigating the computation of minimum spanning trees.