Evolutionary Computation and Convergence to a Pareto Front
Evolutionary Computation and Convergence to a Pareto Front
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发表时间：
1998
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通讯作者：
D. V. Veldhuizen
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作者：
D. V. Veldhuizen
多目标优化问题研究的总体目标之一是发展和定义基于进化计算的多目标优化理论的基础。本文介绍了MOP的相关概念，特别是Pareto最优性的概念。定义了特定的符号，并给出了定理，以确保清楚地理解基于Pareto的进化算法(EA)的实现。然后，给出了一个研究任意进化算法收敛到Pareto前沿的具体实验。这个实验给出了一个定理的基础，该定理表明特定的多目标EA在统计上收敛到帕累托前沿。最后，我们利用这项工作来证明进一步探索基于EC的MOP求解方法的理论基础是正确的。
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. In this paper, we introduce relevant MOP concepts, and the notion of Pareto optimality, in particular. Specific notation is defined and theorems are presented ensuring Paretobased Evolutionary Algorithm (EA) implementations are clearly understood. Then, a specific experiment investigating the convergence of an arbitrary EA to a Pareto front is presented. This experiment gives a basis for a theorem showing a specific multiobjective EA statistically converges to the Pareto front. We conclude by using this work to justify further exploration into the theoretical foundations of EC-based MOP solution methods.