On the Average Number of Maxima in a Set of Vectors and Applications
Journal of the ACM (JACM)
Evolutionary algorithms in data mining: multi-objective performance modeling for direct marketing
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Why Quality Assessment Of Multiobjective Optimizers Is Difficult
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Genetic diversity as an objective in multi-objective evolutionary algorithms
Evolutionary Computation
Algorithms to identify pareto points in multi-dimensional data sets
Algorithms to identify pareto points in multi-dimensional data sets
Conflict, harmony, and independence: relationships in evolutionary multi-criterion optimisation
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
General lower bounds for evolutionary algorithms
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Multi-objective equivalent random search
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
IEEE Transactions on Evolutionary Computation
Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolving modular neural-networks through exaptation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Convergence rates of (1+1) evolutionary multiobjective optimization algorithms
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Adaptive objective space partitioning using conflict information for many-objective optimization
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Comparison-based complexity of multiobjective optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Borg: An auto-adaptive many-objective evolutionary computing framework
Evolutionary Computation
Many objective visual analytics: rethinking the design of complex engineered systems
Structural and Multidisciplinary Optimization
Objective space partitioning using conflict information for solving many-objective problems
Information Sciences: an International Journal
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It has been empirically established that multiobjective evolutionary algorithms do not scale well with the number of conflicting objectives. This paper shows that the convergence rate of all comparison-based multi-objective algorithms, for the Hausdorff distance, is not much better than the convergence rate of the random search under certain conditions. The number of objectives must be very moderate and the framework should hold the following assumptions: the objectives are conflicting and the computational cost is lower bounded by the number of comparisons is a good model. Our conclusions are: (i) the number of conflicting objectives is relevant (ii) the criteria based on comparisons with random-search for multi-objective optimization is also relevant (iii) having more than 3-objectives optimization is very hard. Furthermore, we provide some insight into cross-over operators.