Titlebook: Evolutionary Learning: Advances in Theories and Algorithms; Zhi-Hua Zhou,Yang Yu,Chao Qian Book 2019 Springer Nature Singapore Pte Ltd. 20

使激動(dòng)

https://doi.org/10.1007/978-3-540-72691-3gorithm. Through the derived theorem, the easiest and hardest functions in the pseudo-Boolean function class with a unique global optimal solution are identified for (1+1)-EA with any mutation probability less than 0.5.

ANTI

Ganglion-Cyst

迅速成長(zhǎng)

Joseph C. Schmid,Daniel J. Linfordd on Pareto optimization, we present the PO.SS algorithm for the problem, which is proven to have the state-of-the-art performance and is verified empirically on the applications of influence maximization, information coverage maximization, and sensor placement experiments.

Assemble

Running Time Analysis: Convergence-based Analysisrom bridging two fundamental theoretical issues. The approach is applied to show the exponential lower bound of the expected running time for (1+1)-EA and randomized local search solving the constrained Trap problem.

Constrain

斗志

Running Time Analysis: Comparison and Unificationreducibility relation between two approaches. Consequently, we find that switch analysis can serve as a unified analysis approach, as other approaches can be reduced to switch analysis. This unification also provides a perspective to understand different approaches.

palette

Approximation Analysis: SEIPcompetition among solutions and offers a general characterization of approximation behaviors. The framework is applied to the set cover problem, delivering an .-approximation ratio that matches the asymptotic lower bound.

外向者

Boundary Problems of EAsgorithm. Through the derived theorem, the easiest and hardest functions in the pseudo-Boolean function class with a unique global optimal solution are identified for (1+1)-EA with any mutation probability less than 0.5.

nonsensical

Inaccurate Fitness Evaluationhelpful, while for easy problems, it can be harmful. The findings are verified in the experiments. We also prove that the two common strategies, i.e., threshold selection and sampling, can bring robustness against noise when it is harmful.