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Found 10 papers, 0 papers with code
Runtime Analyses of NSGA-III on Many-Objective Problems
NSGA-II and NSGA-III are two of the most popular evolutionary multi-objective algorithms used in practice.
Analysing the Robustness of NSGA-II under Noise
To our knowledge, this is the first proof that NSGA-II can outperform GSEMO and the first runtime analysis of NSGA-II in noisy optimisation.
Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation
We provide a theoretical analysis of the well-known EMO algorithms GSEMO and NSGA-II to showcase the possible advantages of crossover: we propose classes of "royal road" functions on which these algorithms cover the whole Pareto front in expected polynomial time if crossover is being used.
Runtime Analysis of Fitness-Proportionate Selection on Linear Functions
In contrast to this negative result, we also show that for any linear function with polynomially bounded weights, the EA achieves a polynomial expected runtime if the mutation rate is reduced to $\Theta(1/n^2)$ and the population size is sufficiently large.
Level-Based Analysis of the Univariate Marginal Distribution Algorithm
The facility and generality of our arguments suggest that this is a promising approach to derive bounds on the expected optimisation time of EDAs.
Escaping Local Optima using Crossover with Emergent or Reinforced Diversity
This proves a sizeable advantage of all variants of the ($\mu$+1) GA compared to (1+1) EA, which requires time $\Theta(n^k)$.
Populations can be essential in tracking dynamic optima
It is often claimed that evolutionary algorithms are particularly suitable for dynamic optimisation because a large population can contain different solutions that may be useful in the future.
Self-adaptation of Mutation Rates in Non-elitist Populations
Experimental results indicate that self-adaptation, where parameter settings are encoded in the genomes of individuals, can be effective in continuous optimisation.
Level-Based Analysis of Genetic Algorithms for Combinatorial Optimization
The paper is devoted to upper bounds on run-time of Non-Elitist Genetic Algorithms until some target subset of solutions is visited for the first time.
Level-based Analysis of Genetic Algorithms and other Search Processes
Finally, we prove that the theorem is nearly optimal for the processes considered.
