Customisation in food properties is a challenging task involving optimisation of the production process with the demand to support computational creativity which is geared towards ensuring the presence of alternatives. This paper addresses the personalisation of beer properties in the specific case of craft beers where the production process is more flexible. We investigate the problem by using three swarm intelligence and evolutionary computation techniques that enable brewers to map physico-chemical properties to target organoleptic properties to design a specific brew. While there are several tools, using the original mathematical and chemistry formulas, or machine learning models that deal with the process of determining beer properties based on the pre-determined quantities of ingredients, the next step is to investigate an automated quantitative ingredient selection approach. The process is illustrated by a number of experiments designing craft beers where the results are investigated by "cloning" popular commercial brands based on their known properties. Algorithms performance is evaluated using accuracy, efficiency, reliability, population-diversity, iteration-based improvements and solution diversity. The proposed approach allows for the discovery of new recipes, personalisation and alternative high-fidelity reproduction of existing ones.

中文翻译：

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啤酒感官优化：利用群体智能和进化计算方法

食品特性的定制是一项具有挑战性的任务，涉及优化生产过程，需要支持旨在确保存在替代品的计算创造力。本文讨论了在生产过程更加灵活的精酿啤酒的特定情况下啤酒特性的个性化。我们通过使用三种群体智能和进化计算技术来研究这个问题，这些技术使酿酒商能够将物理化学特性映射到目标感官特性，以设计特定的啤酒。虽然有几种工具，使用原始的数学和化学公式，或机器学习模型来处理基于预先确定的成分数量确定啤酒特性的过程，下一步是研究一种自动定量成分选择方法。该过程通过设计精酿啤酒的许多实验来说明，其中通过基于已知特性“克隆”流行的商业品牌来研究结果。使用准确性、效率、可靠性、种群多样性、基于迭代的改进和解决方案多样性来评估算法性能。所提议的方法允许发现新食谱、个性化和现有食谱的替代高保真复制。效率、可靠性、种群多样性、基于迭代的改进和解决方案的多样性。所提议的方法允许发现新食谱、个性化和现有食谱的替代高保真复制。效率、可靠性、种群多样性、基于迭代的改进和解决方案的多样性。所提议的方法允许发现新食谱、个性化和现有食谱的替代高保真复制。