Multi-Objective Evolutionary Beer Optimisation

TL;DR

This paper presents a multi-objective evolutionary framework for personalising beer recipes by mapping desired properties to ingredient combinations, enabling brewers to generate diverse, optimized craft beer recipes based on user preferences.

Contribution

It introduces a novel multi-objective evolutionary approach for beer recipe personalisation, automating the discovery of diverse recipes aligned with user-defined criteria.

Findings

The framework effectively generates diverse beer recipes.

Multi-objective optimisation outperforms single-objective methods.

The approach is usable and transparent for brewers.

Abstract

Food production is a complex process which can benefit from many optimisation approaches. However, there is growing interest in methods that support customisation of food properties to satisfy individual consumer preferences. This paper addresses the personalisation of beer properties. Having identified components of the production process for craft beers whose production tends to be less standardised, we introduce a system which enables brewers to map the desired beer properties into ingredients dosage and combination. Previously explored approaches include direct use of structural equations as well as global machine learning methods. We introduce a framework which uses an evolutionary method supporting multi-objective optimisation. This work identifies problem-dependent objectives, their associations, and proposes a workflow to automate the discovery of multiple novel recipes based on user-defined criteria. The quality of the solutions generated by the multi-objective optimiser is compared against solutions from multiple runs of the method, and those of a single objective evolutionary technique. This comparison provides a road-map allowing the users to choose among more varied options or to fine-tune one of the favourite identified solution. The experiments presented here demonstrate the usability of the framework as well as the transparency of its criteria.