Convergence analysis of a self-adaptive multi-objective evolutionary algorithm based on grids

Authors:
Yuren Zhou;Jun He
Affiliations:
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, PR China;School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK
Venue:
Information Processing Letters
Year:
2007

Citing 13
Cited 2

Conditions for the convergence of evolutionary algorithms

Journal of Systems Architecture: the EUROMICRO Journal - Special issue on evolutionary computing
Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
How to analyse evolutionary algorithms

Theoretical Computer Science - Natural computing
Combining convergence and diversity in evolutionary multiobjective optimization

Evolutionary Computation
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms

Proceedings of the 1st International Conference on Genetic Algorithms
Evolutionary Search for Minimal Elements in Partially Ordered Finite Sets

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy

Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)

Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Muiltiobjective optimization using nondominated sorting in genetic algorithms

Evolutionary Computation
The convergence of a multi-objective evolutionary algorithm based on grids

ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach

IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation
Running time analysis of multiobjective evolutionary algorithms on pseudo-Boolean functions

IEEE Transactions on Evolutionary Computation

Convergence of multi-objective evolutionary algorithms to a uniformly distributed representation of the Pareto front

Information Sciences: an International Journal
Runtime analysis of a multi-objective evolutionary algorithm for obtaining finite approximations of Pareto fronts

Information Sciences: an International Journal

Quantified Score

Hi-index	0.89

Visualization

Abstract

Evolutionary algorithms have been successfully applied to various multi-objective optimization problems. However, theoretical studies on multi-objective evolutionary algorithms, especially with self-adaption, are relatively scarce. This paper analyzes the convergence properties of a self-adaptive (μ++1)-algorithm. The convergence of the algorithm is defined, and general convergence conditions are studied. Under these conditions, it is proven that the proposed self-adaptive (μ++1)-algorithm converges in probability or almost surely to the Pareto-optimal front.