Drift Analysis with Fitness Levels for Elitist Evolutionary Algorithms [0.03%]
基于适应度层次理论的精英进化算法游程分析方法研究
Jun He,Yuren Zhou
Jun He
The fitness level method is a popular tool for analyzing the hitting time of elitist evolutionary algorithms. Its idea is to divide the search space into multiple fitness levels and estimate lower and upper bounds on the hitting time using ...
Manuel López-Ibáñez,Luís Paquete,Mike Preuss
Manuel López-Ibáñez
BUSTLE: a Versatile Tool for the Evolutionary Learning of STL Specifications from Data [0.03%]
BUSTLE：从数据中演化学习STL规范的通用工具
Federico Pigozzi,Laura Nenzi,Eric Medvet
Federico Pigozzi
Describing the properties of complex systems that evolve over time is a crucial requirement for monitoring and understanding them. Signal Temporal Logic (STL) is a framework that proved to be effective for this aim because it is expressive ...
Estimation of Distribution Algorithm for Grammar-Guided Genetic Programming [0.03%]
基于语法规则的遗传规划的分布估计算法
Pablo Ramos Criado,D Barrios Rolanía,David de la Hoz et al.
Pablo Ramos Criado et al.
Genetic variation operators in grammar-guided genetic programming are fundamental to guide the evolutionary process in search and optimization problems. However, they show some limitations, mainly derived from an unbalanced exploration and ...
Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving [0.03%]
告知式下采样词典选择法：识别有效的训练案例以高效解决问题
Ryan Boldi,Martin Briesch,Dominik Sobania et al.
Ryan Boldi et al.
Genetic Programming (GP) often uses large training sets and requires all individuals to be evaluated on all training cases during selection. Random down-sampled lexicase selection evaluates individuals on only a random subset of the trainin...
Giuseppe Paolo,Miranda Coninx,Alban Laflaquière et al.
Giuseppe Paolo et al.
Learning optimal policies in sparse rewards settings is difficult as the learning agent has little to no feedback on the quality of its actions. In these situations, a good strategy is to focus on exploration, hopefully leading to the disco...
IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics [0.03%]
IOHexperimenter：迭代优化启发式算法的基准测试平台
Jacob de Nobel,Furong Ye,Diederick Vermetten et al.
Jacob de Nobel et al.
We present IOHexperimenter, the experimentation module of the IOHprofiler project. IOHexperimenter aims at providing an easy-to-use and customizable toolbox for benchmarking iterative optimization heuristics such as local search, evolutiona...
Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python [0.03%]
基于特征的连续和约束优化问题的景观分析Python库PFLACCO
Raphael Patrick Prager,Heike Trautmann
Raphael Patrick Prager
The herein proposed Python package pflacco provides a set of numerical features to characterize single-objective continuous and constrained optimization problems. Thereby, pflacco addresses two major challenges in the area optimization. Fir...
A Tri-objective Method for Bi-objective Feature Selection in Classification [0.03%]
模式分类中二目标特征选择的三目标方法
Ruwang Jiao,Bing Xue,Mengjie Zhang
Ruwang Jiao
Minimizing the number of selected features and maximizing the classification performance are two main objectives in feature selection, which can be formulated as a biobjective optimization problem. Due to the complex interactions between fe...
R Paul Wiegand
R Paul Wiegand
Novelty search is a powerful tool for finding diverse sets of objects in complicated spaces. Recent experiments on simplified versions of novelty search introduce the idea that novelty search happens at the level of the archive space, rathe...