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Theory of randomized search heuristics: Foundations and recent developments
Randomized search heuristics such as evolutionary algorithms, genetic algorithms,
evolution strategies, ant colony and particle swarm optimization turn out to be highly …
evolution strategies, ant colony and particle swarm optimization turn out to be highly …
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 …
A survey on recent progress in the theory of evolutionary algorithms for discrete optimization
The theory of evolutionary computation for discrete search spaces has made significant
progress since the early 2010s. This survey summarizes some of the most important recent …
progress since the early 2010s. This survey summarizes some of the most important recent …
Fast genetic algorithms
For genetic algorithms (GAs) using a bit-string representation of length n, the general
recommendation is to take 1/n as mutation rate. In this work, we discuss whether this is …
recommendation is to take 1/n as mutation rate. In this work, we discuss whether this is …
Bioinspired computation in combinatorial optimization: Algorithms and their computational complexity
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation:
Bioinspired computation in combinat Page 1 1/88 Bioinspired Computation in Combinatorial …
Bioinspired computation in combinat Page 1 1/88 Bioinspired Computation in Combinatorial …
The first proven performance guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a combinatorial optimization problem
The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is one of the most prominent
algorithms to solve multi-objective optimization problems. Recently, the first mathematical …
algorithms to solve multi-objective optimization problems. Recently, the first mathematical …
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 …
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. While NSGA-II is used for few objectives such as 2 and 3, NSGA-III is …
used in practice. While NSGA-II is used for few objectives such as 2 and 3, NSGA-III is …
Time complexity of evolutionary algorithms for combinatorial optimization: A decade of results
Computational time complexity analyzes of evolutionary algorithms (EAs) have been
performed since the mid-nineties. The first results were related to very simple algorithms …
performed since the mid-nineties. The first results were related to very simple algorithms …
[HTML][HTML] Mathematical runtime analysis for the non-dominated sorting genetic algorithm II (NSGA-II)
W Zheng, B Doerr - Artificial Intelligence, 2023 - Elsevier
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 …