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An analysis of island models in evolutionary computation.

機譯：進化計算中的孤島模型分析。

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Island models (IMs) are a class of distributed evolutionary algorithms (EAs) in which the population is split into multiple sub-populations called islands. Separate EAs run independently on each island, but they interact by means of migrating individuals. Therefore, IMs are different both from a single-population standard EA, as well as from a set of multiple isolated EAs.; IMs are interesting for several reasons. They have been reported to yield better results than standard EAs. IMs are also advantageous when computational tasks must be distributed across multiple machines because their structure is easy to parallelize. However, despite many studies, no comprehensive theory describing their behavior has been developed. Due to the lack of theory and a complex architecture with many control parameters, setting up IMs has been a trial-and-error process, guided mostly by "rules of thumb."; In this dissertation, I adopt a two-level (intra- and inter-island) view of IMs and show how this approach makes understanding their dynamics easier. They behave very differently than standard EAs, and in order to take full advantage of this, I propose a better utilization of the inter-island level of evolution. In particular, I argue for setups with many relatively small islands, and I also show that compositional evolution may scale to the inter-island level.; The two levels of evolution influence each other, and I analyze this interaction more deeply. Migrations profoundly change the local dynamics and stimulate evolution, which often ultimately results in better performance. I study the role of genetic operators in this behavior and also create mathematical models of after-migration dynamics. This analysis gives us a better understanding of mixing and the survival of genes locally, and these processes in turn determine the type and level of interaction between islands globally. Further, using island heterogeneity enhances the inter-island evolution. Following the study, I analyze IM behavior on a range of test problems, including two complex domains.; This dissertation improves our understanding of the dynamics of IMs and suggests a qualitative change in the way we think about them. This perspective offers new guidelines for configuring IM parameters and opens new directions for future work.
機譯：島嶼模型（IM）是一類分布式進化算法（EA），其中種群被分為多個稱為島嶼的子種群。獨立的EA在每個島嶼上獨立運行，但它們通過遷移的個人進行交互。因此，即時消息與單一種群的標準EA以及一組多個隔離的EA都不相同。 IM之所以有趣，有幾個原因。據(jù)報道，它們比標準EA產(chǎn)生更好的結(jié)果。當(dāng)計算任務(wù)必須分布在多臺計算機上時，IM也是有利的，因為它們的結(jié)構(gòu)易于并行化。但是，盡管進行了許多研究，但尚未開發(fā)出描述其行為的全面理論。由于缺乏理論和具有許多控制參數(shù)的復(fù)雜體系結(jié)構(gòu)，設(shè)置IM一直是一個反復(fù)試驗的過程，主要以“經(jīng)驗法則”為指導(dǎo)。在本文中，我采用了一個兩層（島內(nèi)和島間）的即時消息視圖，并說明了這種方法如何使人們更容易理解即時消息的動態(tài)。它們的行為與標準EA完全不同，為了充分利用這一點，我建議更好地利用島嶼間的進化水平。特別是，我主張建立許多相對較小的島嶼，并且還表明組成演化可能會擴展到島嶼間的水平。進化的兩個層次相互影響，因此我會更深入地分析這種相互作用。遷移深刻地改變了當(dāng)?shù)氐膭討B(tài)并刺激了進化，通常最終會帶來更好的性能。我研究了遺傳算子在這種行為中的作用，并創(chuàng)建了遷移后動力學(xué)的數(shù)學(xué)模型。這項分析使我們對本地基因的混合和存活有了更好的了解，而這些過程反過來又決定了全球島嶼之間相互作用的類型和水平。此外，利用島嶼異質(zhì)性可增強島嶼間的進化。在研究之后，我分析了一系列測試問題上的IM行為，包括兩個復(fù)雜的領(lǐng)域。本文提高了我們對IM動力學(xué)的理解，并提出了我們對IM的思考方式的質(zhì)變。該觀點為配置IM參數(shù)提供了新的指導(dǎo)方針，并為以后的工作打開了新的方向。

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