Design, Architecture, and Engineering with Grammatical Evolution

Fenton, Michael; Byrne, Jonathan; Hemberg, Erik

doi:10.1007/978-3-319-78717-6_13

Cited by 2 publications

(1 citation statement)

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“…This evaluation also produces metadata about the evolutionary process, allowing us to see whether the fitness improves or becomes worse. GE has been successfully applied to different domains, e.g., design, architecture, and engineering [14], where individuals are synthesized to form different engineering design solutions, search-based software engineering [15] optimizing run-time performance in the regular expression language, program synthesis [16] automatically generating caching algorithms, sports analytics [17] predicting matches for the Six Nations Rugby, animation [18] to different simulated animals, design of a cryptographically secure pseudo-random number generator [19], the evolution of complex digital circuits with Sys-temVerilog [20] and optimizing combinational logic circuits [21]. Given these successful use cases, we aim to deliver a novel solution for automatically generating maze scenarios that can be used in the Smart Manufacturing environment and can help us to define the best disposition of things within a space, in order to have an efficient and cost-effective navigation solution.…”

Section: Introductionmentioning

confidence: 99%

MazeGen: A Low-Code Framework for Bootstrapping Robotic Navigation Scenarios for Smart Manufacturing Contexts

Guevara

Margaria

2023

Electronics

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In this research, we describe the MazeGen framework (as a maze generator), which generates navigation scenarios using Grammatical Evolution for robots or drones to navigate. The maze generator uses evolutionary algorithms to create robotic navigation scenarios with different semantic levels along a scenario profile. Grammatical Evolution is a Machine Learning technique from the Evolutionary Computing branch that uses a BNF grammar to describe the language of the possible scenario universe and a numerical encoding of individual scenarios along that grammar. Through a mapping process, it converts new numerical individuals obtained by operations on the parents’ encodings to a new solution by means of grammar. In this context, the grammar describes the scenario elements and some composition rules. We also analyze associated concepts of complexity, understanding complexity as the cost of production of the scenario and skill levels needed to move around the maze. Preliminary results and statistics evidence a low correlation between complexity and the number of obstacles placed, as configurations with more difficult obstacle dispositions were found in the early stages of the evolution process and also when analyzing mazes taking into account their semantic meaning, earlier versions of the experiment not only resulted as too simplistic for the Smart Manufacturing domain, but also lacked correlation with possible real-world scenarios, as was evidenced in our experiments, where the most semantic meaning results had the lowest fitness score. They also show the emerging technology status of this approach, as we still need to find out how to reliably find solvable scenarios and characterize those belonging to the same class of equivalence. Despite being an emerging technology, MazeGen allows users to simplify the process of building configurations for smart manufacturing environments, by making it faster, more efficient, and reproducible, and it also puts the non-expert programmer in the center of the development process, as little boilerplate code is needed.

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