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Leveraging Large Language Models (LLM) like GPT4 in the auto generation of code represents a significant advancement, yet it is not without its challenges. The ambiguity inherent in natural language descriptions of software poses substantial obstacle
Externí odkaz:
http://arxiv.org/abs/2410.18489
Autor:
Internò, Christian, Raponi, Elena, van Stein, Niki, Bäck, Thomas, Olhofer, Markus, Jin, Yaochu, Hammer, Barbara
The rapid proliferation of smart devices coupled with the advent of 6G networks has profoundly reshaped the domain of collaborative machine learning. Alongside growing privacy-security concerns in sensitive fields, these developments have positioned
Externí odkaz:
http://arxiv.org/abs/2405.10271
Generating code from a natural language using Large Language Models (LLMs) such as ChatGPT, seems groundbreaking. Yet, with more extensive use, it's evident that this approach has its own limitations. The inherent ambiguity of natural language presen
Externí odkaz:
http://arxiv.org/abs/2310.04304
Autor:
Brulin, Sebastian, Olhofer, Markus
The design or the optimization of transport systems is a difficult task. This is especially true in the case of the introduction of new transport modes in an existing system. The main reason is, that even small additions and changes result in the eme
Externí odkaz:
http://arxiv.org/abs/2307.14731
Multi-objective optimization problems whose objectives have different evaluation costs are commonly seen in the real world. Such problems are now known as multi-objective optimization problems with heterogeneous objectives (HE-MOPs). So far, however,
Externí odkaz:
http://arxiv.org/abs/2208.12217
Bayesian optimization has emerged at the forefront of expensive black-box optimization due to its data efficiency. Recent years have witnessed a proliferation of studies on the development of new Bayesian optimization algorithms and their application
Externí odkaz:
http://arxiv.org/abs/2206.03301
Most existing multiobjetive evolutionary algorithms (MOEAs) implicitly assume that each objective function can be evaluated within the same period of time. Typically. this is untenable in many real-world optimization scenarios where evaluation of dif
Externí odkaz:
http://arxiv.org/abs/2108.13339
A preference based multi-objective evolutionary algorithm is proposed for generating solutions in an automatically detected knee point region. It is named Automatic Preference based DI-MOEA (AP-DI-MOEA) where DI-MOEA stands for Diversity-Indicator ba
Externí odkaz:
http://arxiv.org/abs/2101.09556
Publikováno v:
In Applied Soft Computing Journal April 2022 119
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