Design of Genetic Algorithms for the Simulation-Based Training of Artificial Neural Networks in the Context of Automated Vehicle Guidance

Yarom, Or Aviv; Jacobitz, Sven; Liu-Henke, Xiaobo

doi:10.1109/me49197.2020.9286464

Cited by 1 publication

(1 citation statement)

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“…The GA flexibility makes them attractive for many optimization problems in practice. In this context, several studies have demonstrated the GA success in different domains such as music [12], games [13] [14], autonomous driving [15], among others. To achieve this success, the definition of an objective function to quantify a candidate solution is fundamental to guarantee the algorithm finding promising solutions.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Investigating Fitness Functions for Search-based Requirements Prioritization

Corezolla

Costa

Souza

et al. 2021

Anais Do XII Computer on the Beach - COTB '21

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It can be challenging for people to select the most relevant requirementamong several software system development options.Requirements prioritization defines the ordering for executing requirementsbased on their priority or importance concerning stakeholders’viewpoints, which is a problematic task. Based on thisproblem, this study aims to present a requirements prioritizationapproach using a genetic algorithm to find optimal solutions, andit can assist in the requirements prioritization activity during thesoftware development process. In this paper, we investigated aset of criteria to create four functions GUT-D, ThS-D, ST, and LT,to assess candidate solutions, i.e., the recommended prioritizedrequirements. We examine the empirical results concerning thepractical approach’s effectiveness and cost computational two experimentsin the evaluation. We found that the 𝐺𝑈𝑇 − 𝐷 fitnessfunction achieved the best fitness value in different settings with90.51% and 98.63%. Besides that, our study demonstrates that the approachis promising to assist requirements prioritization since eachfitness function can be used individually according to companies’necessities.

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Section: Genetic Algorithmmentioning

confidence: 99%