Regret-Based Nash Equilibrium Sorting Genetic Algorithm for Combinatorial Game Theory Problems with Multiple Players [0.03%]
基于遗憾值的纳什均衡排序遗传算法求解多人组合博弈问题
Abdullah Konak,Sadan Kulturel-Konak
Abdullah Konak
We introduce a regret-based fitness assignment strategy for evolutionary algorithms to find Nash equilibria in noncooperative simultaneous combinatorial game theory problems where it is computationally intractable to enumerate all decision ...
J G Falcón-Cardona,M T M Emmerich,C A Coello Coello
J G Falcón-Cardona
The most relevant property that a quality indicator (QI) is expected to have is Pareto compliance, which means that every time an approximation set strictly dominates another in a Pareto sense, the indicator must reflect this. The hypervolu...
Faster Convergence in Multiobjective Optimization Algorithms Based on Decomposition [0.03%]
基于分解的多目标优化算法中收敛速度的加快
Yuri Lavinas,Marcelo Ladeira,Claus Aranha
Yuri Lavinas
The Resource Allocation approach (RA) improves the performance of MOEA/D by maintaining a big population and updating few solutions each generation. However, most of the studies on RA generally focused on the properties of different Resourc...
High-Dimensional Unbalanced Binary Classification by Genetic Programming with Multi-Criterion Fitness Evaluation and Selection [0.03%]
基于多准则适应度评估与选择的遗传程序化方法在高维不均衡数据集上的二分类研究
Wenbin Pei,Bing Xue,Lin Shang et al.
Wenbin Pei et al.
High-dimensional unbalanced classification is challenging because of the joint effects of high dimensionality and class imbalance. Genetic programming (GP) has the potential benefits for use in high-dimensional classification due to its bui...
Francisco Chicano,Gabriela Ochoa,L Darrell Whitley et al.
Francisco Chicano et al.
An optimal recombination operator for two-parent solutions provides the best solution among those that take the value for each variable from one of the parents (gene transmission property). If the solutions are bit strings, the offspring of...
Uncrowded Hypervolume-Based Multiobjective Optimization with Gene-Pool Optimal Mixing [0.03%]
基于非拥挤超体积的基因池最优混合多目标优化
S C Maree,T Alderliesten,P A N Bosman
S C Maree
Domination-based multiobjective (MO) evolutionary algorithms (EAs) are today arguably the most frequently used type of MOEA. These methods, however, stagnate when the majority of the population becomes nondominated, preventing further conve...
Modular Grammatical Evolution for the Generation of Artificial Neural Networks [0.03%]
用于生成人工神经网络的模块化语法进化
Khabat Soltanian,Ali Ebnenasir,Mohsen Afsharchi
Khabat Soltanian
This article presents a novel method, called Modular Grammatical Evolution (MGE), toward validating the hypothesis that restricting the solution space of NeuroEvolution to modular and simple neural networks enables the efficient generation ...
Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multiobjective Evolutionary Algorithm [0.03%]
通过组合多目标进化算法中的许多过渡点演化多模态机器人行为
Joost Huizinga,Jeff Clune
Joost Huizinga
An important challenge in reinforcement learning is to solve multimodal problems, where agents have to act in qualitatively different ways depending on the circumstances. Because multimodal problems are often too difficult to solve directly...
Tobias Glasmachers,Oswin Krause
Tobias Glasmachers
The class of algorithms called Hessian Estimation Evolution Strategies (HE-ESs) update the covariance matrix of their sampling distribution by directly estimating the curvature of the objective function. The approach is practically efficien...
VSD-MOEA: A Dominance-Based Multiobjective Evolutionary Algorithm with Explicit Variable Space Diversity Management [0.03%]
VSD-MOEA：具有明确变量空间多样性管理的 dominance-based 多目标进化算法
Joel Chacón Castillo,Carlos Segura,Carlos A Coello Coello
Joel Chacón Castillo
Most state-of-the-art Multiobjective Evolutionary Algorithms (moeas) promote the preservation of diversity of objective function space but neglect the diversity of decision variable space. The aim of this article is to show that explicitly ...