Transfer Learning Based Co-Surrogate Assisted Evolutionary Bi-Objective Optimization for Objectives with Non-Uniform Evaluation Times [0.03%]
基于迁移学习的协代理辅助进化双目标优化算法用于评估时间非均匀的目标函数
Xilu Wang,Yaochu Jin,Sebastian Schmitt et al.
Xilu Wang et al.
Most existing multiobjective 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 evalua...
Selection Heuristics on Semantic Genetic Programming for Classification Problems [0.03%]
语义遗传规划在分类问题中的选择启发式算法研究
Claudia N Sánchez,Mario Graff
Claudia N Sánchez
Individual semantics have been used for guiding the learning process of Genetic Programming. Novel genetic operators and different ways of performing parent selection have been proposed with the use of semantics. The latter is the focus of ...
Using Well-Understood Single-Objective Functions in Multiobjective Black-Box Optimization Test Suites [0.03%]
在多目标黑盒优化测试套件中使用众所周知的单目标函数
Dimo Brockhoff,Anne Auger,Nikolaus Hansen et al.
Dimo Brockhoff et al.
Several test function suites are being used for numerical benchmarking of multiobjective optimization algorithms. While they have some desirable properties, such as well-understood Pareto sets and Pareto fronts of various shapes, most of th...
Runtime Analysis of Restricted Tournament Selection for Bimodal Optimisation [0.03%]
双模优化受限锦标赛选择的运行时间分析
Edgar Covantes Osuna,Dirk Sudholt
Edgar Covantes Osuna
Niching methods have been developed to maintain the population diversity, to investigate many peaks in parallel, and to reduce the effect of genetic drift. We present the first rigorous runtime analyses of restricted tournament selection (R...
The Univariate Marginal Distribution Algorithm Copes Well with Deception and Epistasis [0.03%]
单变量边际分布算法对欺骗和上位性处理良好
Benjamin Doerr,Martin S Krejca
Benjamin Doerr
In their recent work, Lehre and Nguyen (2019) show that the univariate marginal distribution algorithm (UMDA) needs time exponential in the parent populations size to optimize the DeceptiveLeadingBlocks (DLB) problem. They conclude from thi...
Shape-Constrained Symbolic Regression-Improving Extrapolation with Prior Knowledge [0.03%]
形状约束的符号回归-利用先验知识改进外推
G Kronberger,F O de Franca,B Burlacu et al.
G Kronberger et al.
We investigate the addition of constraints on the function image and its derivatives for the incorporation of prior knowledge in symbolic regression. The approach is called shape-constrained symbolic regression and allows us to enforce, for...
Environmental Adaptation of Robot Morphology and Control Through Real-World Evolution [0.03%]
通过现实世界进化实现机器人形态和控制的环境适应
T F Nygaard,C P Martin,D Howard et al.
T F Nygaard et al.
Robots operating in the real world will experience a range of different environments and tasks. It is essential for the robot to have the ability to adapt to its surroundings to work efficiently in changing conditions. Evolutionary robotics...
Iterated Local Search and Other Algorithms for Buffered Two-Machine Permutation Flow Shops with Constant Processing Times on One Machine [0.03%]
含缓冲区的两台机器恒定加工时间排列流水 shop问题的迭代局部搜索及其他算法
Hoang Thanh Le,Philine Geser,Martin Middendorf
Hoang Thanh Le
The two-machine permutation flow shop scheduling problem with buffer is studied for the special case that all processing times on one of the two machines are equal to a constant c. This case is interesting because it occurs in various appli...
Iterated Local Search and Other Algorithms for Buffered Two-Machine Permutation Flow Shops with Constant Processing Times on One Machine [0.03%]
含缓冲区的两台 permutation 流水车间问题的算法研究
Hoang Thanh Le,Philine Geser,Martin Middendorf
Hoang Thanh Le
The two-machine permutation flow shop scheduling problem with buffer is studied for the special case that all processing times on one of the two machines are equal to a constant c. This case is interesting because it occurs in various appli...
Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions [0.03%]
适应变化环境条件的自主学习的进化塑性
Anil Yaman,Giovanni Iacca,Decebal Constantin Mocanu et al.
Anil Yaman et al.
A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasti...