Data-driven evolutionary optimizationDistributed optimizationFederated learningRBFN surrogate modelData-driven evolutionary optimization has witnessed great success in solving complex real-world optimization problems. However, existing data-driven optimization algorithms require that all data are centrally stored, ...
Evolutionary algorithmsGenetic programmingIn this paper we propose a data-driven approach for the construction of survey-based indicators using large data sets. We make use of agents' expectations about a wide range of economic variables contained in the World Economic Survey, which is a tendency ...
Data-driven modelsare based on data. Machine and statistical learning algorithms are used for building such models from data. For this, data need to be explored, usually several models are considered, and finally a model is built through the application of a particular algorithm. This model will...
For computationally intensive problems, data-driven evolutionary algorithms (DDEAs) are advantageous for low computational budgets because they build surrogate models based on historical data to approximate the expensive evaluation. Real-world optimization problems are highly susceptible to noisy data, but ...
Evol is clear dsl for composable evolutionary algorithms that optimised for joy. Installation We currently support python3.6 and python3.7 and you can install it via pip. pip install evol Documentation For more details you can read the docs but we advice everyone to get start by first checking...
Index Terms—Data science, data-driven optimization, evolutionary algorithms (EAs), machine learning, model management, surrogate. 大多数进化优化算法假定目标和约束函数的评估是直接的。然而,在解决许多现实世界的优化问题时,这种目标函数可能不存在。相反,必须进行计算上昂贵的数值模拟或昂贵的物理实验来进行适配...
Given its advantages in optimization performance and versatility, it is worth utilizing in other data-driven scenarios. Additionally, the proposed adaptive strategies can be embedded into existing state-of-the-art evolutionary algorithms for further improvement. For the industry, this paper showcases a...
ings, Fuzzy Control, Evolutionary Algorithms, Data Driven Generation. 1 Introduction Ambient Intelligence (AmI) is nowadays an active re- search field. AmI deals with the development of a new paradigm where people are immersed in a digital envi- ...
(FRBSs) serve as fundamental model frameworks, which are alternatives to statistical inference methods, while evolutionary algorithms (EAs), swarm intelligence (SI), and machine learning (ML) methods provide learning and optimization abilities for calibrating and improving the intelligent or statistical ...
Evolutionary algorithms46,47,75, brute-force search48 or more principled Bayesian techniques76 are then used to minimize the distances between observed and model-derived features. While these likelihood-free, simulation-based methods can be applied to more complex models they exhibit disadvantages: the...