Open AccessProceedings Article
On evolutionary optimization with approximate fitness functions
Yaochu Jin,Markus Olhofer,Bernhard Sendhoff +2 more
- pp 786-793
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TLDR
It is found that the evolutionary algorithm will converge incorrectly if the approximate model has false optima, so two strategies to control the evolution process are introduced and methods to eliminate false minima in neural network training are proposed.Abstract:
The evaluation of the quality of solutions is usually very time-consuming in design optimization. Therefore, time-efficient approximate models can be particularly beneficial for the evaluation when evolutionary algorithms are applied. In this paper, the convergence property of an evolution strategy (ES) with neural network based fitness evaluations is investigated. It is found that the evolutionary algorithm will converge incorrectly if the approximate model has false optima. To address this problem, two strategies to control the evolution process are introduced. In addition, methods to eliminate false minima in neural network training are proposed. The effectiveness of the methods are shown with simulation studies on the Ackley function and the Rosenbrock function.read more
Citations
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Evolutionary optimization in uncertain environments-a survey
Yaochu Jin,Jürgen Branke +1 more
TL;DR: This paper attempts to provide a comprehensive overview of the related work within a unified framework on addressing different uncertainties in evolutionary computation, which has been scattered in a variety of research areas.
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A comprehensive survey of fitness approximation in evolutionary computation
TL;DR: A comprehensive survey of the research on fitness approximation in evolutionary computation is presented, main issues like approximation levels, approximate model management schemes, model construction techniques are reviewed and open questions and interesting issues in the field are discussed.
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Surrogate-assisted evolutionary computation: Recent advances and future challenges
TL;DR: This paper provides a concise overview of the history and recent developments in surrogate-assisted evolutionary computation and suggests a few future trends in this research area.
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A framework for evolutionary optimization with approximate fitness functions
TL;DR: A framework for managing approximate models in generation-based evolution control is proposed, well suited for parallel evolutionary optimization, which is able to guarantee the correct convergence of the evolutionary algorithm, as well as to reduce the computation cost as much as possible.
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Generalizing Surrogate-Assisted Evolutionary Computation
TL;DR: The generalized evolutionary framework focuses on attaining reliable search performance in the surrogate-assisted evolutionary framework by working on two major issues: to mitigate the 'curse of uncertainty' robustly, and to benefit from the 'bless of uncertainty.'
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