Evolutionary Computation and Convergence to a Pareto Front
@inproceedings{Veldhuizen1998EvolutionaryCA,
title={Evolutionary Computation and Convergence to a Pareto Front},
author={David A. van Veldhuizen},
year={1998}
}Research into solving multiobjective optimization problems (MOP) has as one of its an overall goals that of developing and defining foundations of an Evolutionary Computation (EC)-based MOP theory. [] Key Result We conclude by using this work to justify further exploration into the theoretical foundations of EC-based MOP solution methods.
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313 Citations
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This paper provides a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions and shows that elitism is shown to be an important factor for improving evolutionary multiobjectives search.
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Two new performance measures computing the convergence towards the Pareto front and the solution diversity on the PAREto front are proposed and an outlook on how to further deepen insight in performance measures of MOEAs is given.
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The paper proves the convergence of MOEAs with certain features, and the process of proof has shown that it is reasonable to regard Pknown achieved from the final results ofMOEAs as Ptrue or the approximated Pareto optimal set.
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- 2016
A novel entropy-based dissimilarity measure is proposed that helps identify on the fly the number of generations beyond which an algorithm stabilizes, implying that either a good approximation has been obtained or that it cannot be obtained due to the stagnation of the algorithm in the search space.
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An entropy-based multi-objective evolutionary algorithm with an enhanced elite mechanism (E-MOEA), which improves the convergence and diversity of solution set in MOPs effectively and accelerates the population to approach the true Pareto front at the early stage of the evolution process.
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