Multifactorial Evolution: Toward Evolutionary Multitasking
Multifactorial Evolution: Toward Evolutionary Multitasking
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DOI：
10.1109/tevc.2015.2458037
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发表时间：
2016-06-01
影响因子：
14.3
通讯作者：
Feng, Liang
中科院分区：
文献类型：
--
作者：
Gupta, Abhishek;Ong, Yew-Soon;Feng, Liang
进化算法的设计通常集中在一次有效地解决单个优化问题。尽管基于人口的搜索隐含着并行性，但还没有尝试进行多任务处理，即，使用单个进化个体群体同时解决多个优化问题。因此，本文介绍了进化多任务作为一种新的范式在优化和进化计算领域。我们首先形式化的进化多任务的概念，然后提出了一个算法来处理这样的问题。该方法的灵感来自多因素遗传的生物文化模型，该模型解释了通过遗传和文化因素的相互作用将复杂的发育特征传递给后代。此外，我们还开发了一个跨域优化平台，可以同时解决各种问题。数值实验揭示了多任务环境中隐式遗传转移的几个潜在优势。最值得注意的是，我们发现，精制遗传物质的创建和转移往往会导致加速收敛的各种复杂的优化功能。
The design of evolutionary algorithms has typically been focused on efficiently solving a single optimization problem at a time. Despite the implicit parallelism of population-based search, no attempt has yet been made to multitask, i.e., to solve multiple optimization problems simultaneously using a single population of evolving individuals. Accordingly, this paper introduces evolutionary multitasking as a new paradigm in the field of optimization and evolutionary computation. We first formalize the concept of evolutionary multitasking and then propose an algorithm to handle such problems. The methodology is inspired by biocultural models of multifactorial inheritance, which explain the transmission of complex developmental traits to offspring through the interactions of genetic and cultural factors. Furthermore, we develop a cross-domain optimization platform that allows one to solve diverse problems concurrently. The numerical experiments reveal several potential advantages of implicit genetic transfer in a multitasking environment. Most notably, we discover that the creation and transfer of refined genetic material can often lead to accelerated convergence for a variety of complex optimization functions.