Learnable Evolutionary Search Across Heterogeneous Problems via Kernelized Autoencoding
Learnable Evolutionary Search Across Heterogeneous Problems via Kernelized Autoencoding
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DOI：
10.1109/tevc.2021.3056514
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发表时间：
2021-06
影响因子：
14.3
通讯作者：
Lei Zhou;Liang Feng;Abhishek Gupta;Y. Ong
中科院分区：
文献类型：
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作者：
Lei Zhou;Liang Feng;Abhishek Gupta;Y. Ong
近年来，从过去的搜索经验中设计具有学习能力的进化算法引起了越来越多的研究兴趣。已经证明，如果适当地利用，过去搜索经验中嵌入的知识可以极大地加快进化过程。自动编码进化搜索(AEES)是最近提出的一种搜索范式，它采用单层去噪自动编码器来建立两个问题之间的映射，将每个问题的解分别配置为自动编码器的输入和输出。所学习的映射使跨具有不同属性的不同问题领域进行知识转移成为可能。它在学习和传递过去搜索经验中的知识以促进对各种优化问题的进化搜索方面表现出了良好的性能。然而，尽管AEES取得了成功，但线性自动编码模型不能捕捉到映射构造中使用的解集之间的非线性关系。受此启发，在本文中，我们设计了一个核化的自动编码器来构造再生核Hilbert空间(RKHS)中的映射，在该空间中可以很容易地捕捉问题解之间的非线性。重要的是，提出的核化自动编码方法也具有闭合形式的解，在进化搜索中不会带来太大的计算负担。在此基础上，提出了一种核化自动编码进化搜索(KAES)范式，该范式在搜索过程中自适应地选择线性和核化自动编码，以追求跨问题域的有效知识转移。为了验证所提KES的有效性，对基准多目标优化问题和实际车辆耐撞性设计问题进行了全面的实证研究。
The design of the evolutionary algorithm with learning capability from past search experiences has attracted growing research interests in recent years. It has been demonstrated that the knowledge embedded in the past search experience can greatly speed up the evolutionary process if properly harnessed. Autoencoding evolutionary search (AEES) is a recently proposed search paradigm, which employs a single-layer denoising autoencoder to build the mapping between two problems by configuring the solutions of each problem as the input and output for the autoencoder, respectively. The learned mapping makes it possible to perform knowledge transfer across heterogeneous problem domains with diverse properties. It has shown a promising performance of learning and transferring the knowledge from past search experiences to facilitate the evolutionary search on a variety of optimization problems. However, despite the success enjoyed by AEES, the linear autoencoding model cannot capture the nonlinear relationship between the solution sets used in the mapping construction. Taking this cue, in this article, we devise a kernelized autoencoder to construct the mapping in a reproducing kernel Hilbert space (RKHS), where the nonlinearity among problem solutions can be captured easily. Importantly, the proposed kernelized autoencoding method also holds a closed-form solution which will not bring much computational burden in the evolutionary search. Furthermore, a kernelized autoencoding evolutionary-search (KAES) paradigm is proposed that adaptively selects the linear and kernelized autoencoding along the search process in pursuit of effective knowledge transfer across problem domains. To validate the efficacy of the proposed KAES, comprehensive empirical studies on both benchmark multiobjective optimization problems as well as real-world vehicle crashworthiness design problem are presented.