An analysis on recombination in multi-objective evolutionary optimization

DOI10.1016/j.artint.2013.09.002zbMath1334.68203OpenAlexW2015353056MaRDI QIDQ490439

Chao Qian, Yang Yu, Zhi-Hua Zhou

Publication date: 27 August 2015

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.artint.2013.09.002

zbMATH Keywords

computational complexitymulti-objective optimizationrecombinationevolutionary algorithmscrossoverrunning time

Mathematics Subject Classification ID

Analysis of algorithms and problem complexity (68Q25) Multi-objective and goal programming (90C29) Approximation methods and heuristics in mathematical programming (90C59) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (10)

Variable solution structure can be helpful in evolutionary optimization ⋮ Analysing the robustness of evolutionary algorithms to noise: refined runtime bounds and an example where noise is beneficial ⋮ Design and analysis of diversity-based parent selection schemes for speeding up evolutionary multi-objective optimisation ⋮ Mathematical runtime analysis for the non-dominated sorting genetic algorithm II (NSGA-II) ⋮ Optimization methods for regularization-based ill-posed problems: a survey and a multi-objective framework ⋮ Lower bounds on the runtime of crossover-based algorithms via decoupling and family graphs ⋮ Exponential slowdown for larger populations: the \(( \mu + 1)\)-EA on monotone functions ⋮ Optimizing group learning: an evolutionary computing approach ⋮ Maximizing submodular or monotone approximately submodular functions by multi-objective evolutionary algorithms ⋮ On the effectiveness of immune inspired mutation operators in some discrete optimization problems

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• On the approximation ability of evolutionary optimization with application to minimum set cover
• Bioinspired computation in combinatorial optimization. Algorithms and their computational complexity
• Minimum spanning trees made easier via multi-objective optimization
• Expected runtimes of a simple evolutionary algorithm for the multi-objective minimum spanning tree problem
• Randomized local search, evolutionary algorithms, and the minimum spanning tree problem
• On the spanning trees of weighted graphs
• On spanning tree problems with multiple objectives
• On bicriterion minimal spanning trees: An approximation
• Evolutionary algorithms. The role of mutation and recombination
• On the analysis of the \((1+1)\) evolutionary algorithm
• The problem of the optimal biobjective spanning tree
• On the convergence of multiobjective evolutionary algorithms
• Genetic algorithm approach on multi-criteria minimum spanning tree problem
• The analysis of evolutionary algorithms -- A proof that crossover really can help
• Real royal road functions -- where crossover provably is essential
• A new approach to estimating the expected first hitting time of evolutionary algorithms
• The multi-criteria minimum spanning tree problem based genetic algorithm
• The one-dimensional Ising model: mutation versus recombination
• Multicriteria Optimization
• Drift analysis and average time complexity of evolutionary algorithms

This page was built for publication: An analysis on recombination in multi-objective evolutionary optimization