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Deep optimisation: Multi-scale Evolution by Inducing and Searching in Deep Representations 24th

Deep Optimisation: Multi-scale Evolution by Inducing and Sea...

24th International Conference on the Applications of Evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Caldwell, Jamie Knowles, Joshua Thies, Christoph Kubacki, Filip Watson, Richard Univ Southampton Agents Interact & Complex AIC Res Grp Southampton SO17 1BJ Hants England Invenia Labs Cambridge CB2 1AW England

ISBN: (纸本)9783030726980;9783030726997

The ability of evolutionary processes to innovate and scale up over long periods of time, observed in nature, remains a central mystery in evolutionary biology, and a challenge for algorithm designers to emulate and explain in evolutionary computation (EC). The Major Transitions in Evolution is a compelling theory that explains evolvability through a multi-scale process whereby individuality (and hence selection and variation) is continually revised by the formation of associations between formerly independent entities, a process still not fully explored in EC. Deep optimisation (DO) is a new type of model-building optimization algorithm (MBOA) that exploits deep learning methods to enable multi-scale optimization. DO uses an autoencoder model to induce a multi-level representation of solutions, capturing the relationships between the lower-level units that contribute to the quality of a solution. Variation and selection are then performed within the induced representations, causing model-informed changes to multiple solution variables simultaneously. Here, we first show that DO has impressive performance compared with other leading MBOAs (and other rival methods) on multiple knapsack problems, a standard combinatorial optimization problem of general interest. Going deeper, we then carry out a detailed investigation to understand the differences between DO and other MBOAs, identifying key problem characteristics where other MBOAs are afflicted by exponential running times, and DO is not. This study serves to concretize our understanding of the Major Transitions theory, and why that leads to evolvability, and also provides a strong motivation for further investigation of deep learning methods in optimization.

关键词： model-building optimisation algorithms Deep autoencoder Multi-scale search Problem structure

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Deep optimisation: Transitioning the Scale of Evolutionary Search by Inducing and Searching in Deep Representations

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SN Computer Science 2022年第3期3卷 253页

作者： Caldwell, Jamie Knowles, Joshua Thies, Christoph Kubacki, Filip Watson, Richard Agents Interaction and Complexity (AIC) Research Group Southampton University Southampton SO17 1BJ United Kingdom School of Computer Science University of Birmingham Birmingham B15 2TT United Kingdom Invenia Labs Cambridge CB2 1AW United Kingdom

We investigate the optimisation capabilities of an algorithm inspired by the Evolutionary Transitions in Individuality. In these transitions, the natural evolutionary process is repeatedly rescaled through successive levels of biological organisation. Each transition creates new higher-level evolutionary units that combine multiple units from the level below. We call the algorithm Deep optimisation (DO) to recognise both its use of deep learning methods and the multi-level rescaling of biological evolutionary processes. The evolutionary model used in DO is a simple hill-climber, but, as higher-level representations are learned, the hill-climbing process is repeatedly rescaled to operate in successively higher-level representations. The transition process is based on a deep learning neural network (NN), specifically a deep auto-encoder. Our experiments with DO start with a study using the NP-hard problem, multiple knapsack (MKP). Comparing with state-of-the-art model-building optimisation algorithms (MBOAs), we show that DO finds better solutions to MKP instances and does so without using a problem-specific repair operator. A second, much more in-depth investigation uses a class of configurable problems to understand more precisely the distinct problem characteristics that DO can solve that other MBOAs cannot. Specifically, we observe a polynomial vs exponential scaling distinction where DO is the only algorithm to show polynomial scaling for all problems. We also demonstrate that some problem characteristics need a deep network in DO. In sum, our findings suggest that the use of deep learning principles have significant untapped potential in combinatorial optimisation. Moreover, we argue that natural evolution could be implementing something like DO, and the evolutionary transitions in individuality are the observable result. © 2022, The Author(s).

关键词： Deep autoencoder model-building optimisation algorithms Multi-scale search Problem structure

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