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

冒號(hào)

PreliminariesThis chapter introduces preliminaries. Including basic evolutionary algorithms, pseudo-Boolean functions for theoretical studies, and basic knowledge for analyzing running time complexity of evolutionary algorithms.

案發(fā)地點(diǎn)

RecombinationThis chapter studies the influence of recombination operators. We show that, in multi-objective evolutionary optimization, recombination operators are useful for multi-objective evolutionary optimization by accelerating the filling of the Pareto front. This principle may also hold in more situations.

免除責(zé)任

小臼

PopulationThis chapter studies the influence of population on evolutionary algorithms. We show that, on one hand, population is unexpected for simple functions such as OneMax and LeadningOnes by derving the lower running time bound, and on the other hand, in the presence of noise, using population can enhance the robustness against noise.

拋媚眼

職業(yè)拳擊手

Hemiparesis

Thyroid-Gland

MIRTH

s for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary opt978-981-13-5956-9

COST

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.