`` Direct Search'' Solution of Numerical and Statistical Problems
Journal of the ACM (JACM)
The theory of evolution strategies
The theory of evolution strategies
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Evolutionary computation and Wright's equation
Theoretical Computer Science - Natural computing
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
The Estimation of Distributions and the Minimum Relative Entropy Principle
Evolutionary Computation
Comparison-based algorithms are robust and randomized algorithms are anytime
Evolutionary Computation
On the hardness of offline multi-objective optimization
Evolutionary Computation
Lower Bounds for Evolution Strategies Using VC-Dimension
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Covariance Matrix Adaptation Revisited --- The CMSA Evolution Strategy ---
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Convergence rates of SMS-EMOA on continuous bi-objective problem classes
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
General lower bounds for evolutionary algorithms
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
IEEE Transactions on Evolutionary Computation
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Several comparison-based complexity results have been published recently, including multi-objective optimization. However, these results are, in the multiobjective case, quite pessimistic, due to the huge family of fitness functions considered. Combining assumptions on fitness functions and traditional comparison-based assumptions, we get more realistic bounds emphasizing the importance of reducing the number of conflicting objectives for reducing the runtime of multiobjective optimization. The approach can in particular predict lower bounds on the computation time, depending on the type of requested convergence: pointwise, or to the whole Pareto set. Also, a new (untested yet) algorithm is proposed for approximating the whole Pareto set.