A first mathematical runtime analysis of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II)
W Zheng, Y Liu, B Doerr - Proceedings of the AAAI conference on …, 2022 - ojs.aaai.org
The non-dominated sorting genetic algorithm II (NSGA-II) is the most intensively used multi-
objective evolutionary algorithm (MOEA) in real-world applications. However, in contrast to …
objective evolutionary algorithm (MOEA) in real-world applications. However, in contrast to …
[BOOK][B] Evolutionary learning: Advances in theories and algorithms
Many machine learning tasks involve solving complex optimization problems, such as
working on non-differentiable, non-continuous, and non-unique objective functions; in some …
working on non-differentiable, non-continuous, and non-unique objective functions; in some …
A first runtime analysis of the NSGA-II on a multimodal problem
Very recently, the first mathematical runtime analyses of the multiobjective evolutionary
optimizer nondominated sorting genetic algorithm II (NSGA-II) have been conducted. We …
optimizer nondominated sorting genetic algorithm II (NSGA-II) have been conducted. We …
A proof that using crossover can guarantee exponential speed-ups in evolutionary multi-objective optimisation
Evolutionary algorithms are popular algorithms for multiobjective optimisation (also called
Pareto optimisation) as they use a population to store trade-offs between different objectives …
Pareto optimisation) as they use a population to store trade-offs between different objectives …
Runtime analysis of the SMS-EMOA for many-objective optimization
W Zheng, B Doerr - Proceedings of the AAAI Conference on Artificial …, 2024 - ojs.aaai.org
The widely used multiobjective optimizer NSGA-II was recently proven to have considerable
difficulties in many-objective optimization. In contrast, experimental results in the literature …
difficulties in many-objective optimization. In contrast, experimental results in the literature …
Subset selection by Pareto optimization
Selecting the optimal subset from a large set of variables is a fundamental problem in
various learning tasks such as feature selection, sparse regression, dictionary learning, etc …
various learning tasks such as feature selection, sparse regression, dictionary learning, etc …
Runtime analysis for the NSGA-II: Provable speed-ups from crossover
Very recently, the first mathematical runtime analyses for the NSGA-II, the most common
multi-objective evolutionary algorithm, have been conducted. Continuing this research …
multi-objective evolutionary algorithm, have been conducted. Continuing this research …
Stochastic population update can provably be helpful in multi-objective evolutionary algorithms
Evolutionary algorithms (EAs) have been widely and successfully applied to solve multi-
objective optimization problems, due to their nature of population-based search. Population …
objective optimization problems, due to their nature of population-based search. Population …
Better running time of the non-dominated sorting genetic algorithm II (NSGA-II) by using stochastic tournament selection
C Bian, C Qian - International Conference on Parallel Problem Solving …, 2022 - Springer
Evolutionary algorithms (EAs) have been widely used to solve multi-objective optimization
problems, and have become the most popular tool. However, the theoretical foundation of …
problems, and have become the most popular tool. However, the theoretical foundation of …
Theoretical analyses of multi-objective evolutionary algorithms on multi-modal objectives: (hot-off-the-press track at GECCO 2021)
B Doerr, W Zheng - Proceedings of the Genetic and Evolutionary …, 2021 - dl.acm.org
Previous theory work on multi-objective evolutionary algorithms considers mostly easy
problems that are composed of unimodal objectives. This paper takes a first step towards a …
problems that are composed of unimodal objectives. This paper takes a first step towards a …