Isidro M Alvarez, Trung B Nguyen, Will N Browne, Mengjie Zhang
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引用次数: 0
Abstract
Evolutionary Computation (EC) often throws away learned knowledge as it is reset for each new problem addressed. Conversely, humans can learn from small-scale problems, retain this knowledge (plus functionality) and then successfully reuse them in larger-scale and/or related problems. Linking solutions to problems together has been achieved through layered learning, where an experimenter sets a series of simpler related problems to solve a more complex task. Recent works on Learning Classifier Systems (LCSs) has shown that knowledge reuse through the adoption of Code Fragments, GP-like tree-based programs, is plausible. However, random reuse is inefficient. Thus, the research question is how LCS can adopt a layered-learning framework, such that increasingly complex problems can be solved efficiently? An LCS (named XCSCF*) has been developed to include the required base axioms necessary for learning, refined methods for transfer learning and learning recast as a decomposition into a series of subordinate problems. These subordinate problems can be set as a curriculum by a teacher, but this does not mean that an agent can learn from it. Especially if it only extracts over-fitted knowledge of each problem rather than the underlying scalable patterns and functions. Results show that from a conventional tabula rasa, with only a vague notion of what subordinate problems might be relevant, XCSCF* captures the general logic behind the tested domains and therefore can solve any n-bit Multiplexer, n-bit Carry-one, n-bit Majority-on, and n-bit Even-parity problems. This work demonstrates a step towards continual learning as learned knowledge is effectively reused in subsequent problems.
进化计算(EC)通常会丢弃已学知识,因为每解决一个新问题,都要重新设置这些知识。相反,人类可以从小规模的问题中学习,保留这些知识(以及功能),然后成功地在更大规模和/或相关的问题中重复使用。通过分层学习,实验者可以设置一系列较简单的相关问题来解决较复杂的任务,从而将问题的解决方案联系在一起。最近关于学习分类器系统(LCS)的研究表明,通过采用代码片段(类似于 GP 的树状程序)进行知识重用是可行的。然而,随机重用的效率很低。因此,研究的问题是学习分类系统如何采用分层学习框架,从而高效地解决日益复杂的问题?我们开发了一种 LCS(名为 XCSCF*),其中包括学习所需的基本公理、迁移学习的精炼方法以及分解为一系列下级问题的学习重构。这些下属问题可以由教师设置为课程,但这并不意味着代理可以从中学习。特别是如果它只是提取每个问题的过度拟合知识,而不是潜在的可扩展模式和函数。结果表明,XCSCF*能从传统的表格中捕捉到测试领域背后的一般逻辑,因此能解决任何n位多路复用器、n位携带一、n位多数开和n位偶奇偶问题。这项工作展示了向持续学习迈出的一步,因为学到的知识可以在后续问题中有效地重复使用。
期刊介绍:
Evolutionary Computation is a leading journal in its field. It provides an international forum for facilitating and enhancing the exchange of information among researchers involved in both the theoretical and practical aspects of computational systems drawing their inspiration from nature, with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, and genetic programming. It welcomes articles from related fields such as swarm intelligence (e.g. Ant Colony Optimization and Particle Swarm Optimization), and other nature-inspired computation paradigms (e.g. Artificial Immune Systems). As well as publishing articles describing theoretical and/or experimental work, the journal also welcomes application-focused papers describing breakthrough results in an application domain or methodological papers where the specificities of the real-world problem led to significant algorithmic improvements that could possibly be generalized to other areas.