Upgrades of Genetic Programming for Data-Driven Modeling of Time Series [0.03%]
数据驱动的时间序列模型的遗传编程改进方法研究
A Murari,E Peluso,L Spolladore et al.
A Murari et al.
In many engineering fields and scientific disciplines, the results of experiments are in the form of time series, which can be quite problematic to interpret and model. Genetic programming tools are quite powerful in extracting knowledge fr...
Treed Gaussian Process Regression for Solving Offline Data-Driven Continuous Multiobjective Optimization Problems [0.03%]
基于树的高斯过程回归在离线数据驱动连续多目标优化问题中的应用
Atanu Mazumdar,Manuel López-Ibáñez,Tinkle Chugh et al.
Atanu Mazumdar et al.
For offline data-driven multiobjective optimization problems (MOPs), no new data is available during the optimization process. Approximation models (or surrogates) are first built using the provided offline data, and an optimizer, for examp...
Preethi Sankineni,Andrew M Sutton
Preethi Sankineni
Recently, Rowe and Aishwaryaprajna (2019) introduced a simple majority vote technique that efficiently solves Jump with large gaps, OneMax with large noise, and any monotone function with a polynomial-size image. In this paper, we identify ...
Efficient Quality Diversity Optimization of 3D Buildings through 2D Pre-Optimization [0.03%]
基于二维预优化的三维建筑质量多样性优化方法
Alexander Hagg,Martin L Kliemank,Alexander Asteroth et al.
Alexander Hagg et al.
Quality diversity algorithms can be used to efficiently create a diverse set of solutions to inform engineers' intuition. But quality diversity is not efficient in very expensive problems, needing hundreds of thousands of evaluations. Even ...
Theoretical Analyses of Multiobjective Evolutionary Algorithms on Multimodal Objectives [0.03%]
多目标进化算法在多模态函数上的理论分析
Weijie Zheng,Benjamin Doerr
Weijie Zheng
Multiobjective evolutionary algorithms are successfully applied in many real-world multiobjective optimization problems. As for many other AI methods, the theoretical understanding of these algorithms is lagging far behind their success in ...
Kenneth De Jong,Emma Hart
Kenneth De Jong
We reflect on 30 years of the journal Evolutionary Computation. Taking the papers published in the first volume in 1993 as a springboard, as the founding and current Editors-in-Chief, we comment on the beginnings of the field, evaluate the ...
Personal Reflections on Some Early Work in Evolving Strategies in the Iterated Prisoner's Dilemma [0.03%]
关于迭代囚徒困境中早期研究的几点思考
David B Fogel
David B Fogel
On the occasion of the 30-year anniversary of the Evolutionary Computation journal, I was invited by Professor Hart to offer some reflections on the article on evolving behaviors in the iterated prisoner's dilemma that I contributed to its ...
Zbigniew Michalewicz
Zbigniew Michalewicz
This paper presents a personal account of the author's 35 years "adventure" with Evolutionary Computation-from the first encounter in 1988 and many years of academic research through to working full-time in business-successfully implementin...
Using Decomposed Error for Reproducing Implicit Understanding of Algorithms [0.03%]
基于分解误差的算法隐式理解复制方法研究
Caitlin A Owen,Grant Dick,Peter A Whigham
Caitlin A Owen
Reproducibility is important for having confidence in evolutionary machine learning algorithms. Although the focus of reproducibility is usually to recreate an aggregate prediction error score using fixed random seeds, this is not sufficien...
Evolutionary and Estimation of Distribution Algorithms for Unconstrained, Constrained, and Multiobjective Noisy Combinatorial Optimisation Problems [0.03%]
求解无约束、有约束及多目标组合优化问题的进化与分布估计算法
Aishwaryaprajna,Jonathan E Rowe
Aishwaryaprajna
We present an empirical study of a range of evolutionary algorithms applied to various noisy combinatorial optimisation problems. There are three sets of experiments. The first looks at several toy problems, such as OneMax and other linear ...