J
Jinglu Hu
Researcher at Waseda University
Publications - 396
Citations - 4285
Jinglu Hu is an academic researcher from Waseda University. The author has contributed to research in topics: Artificial neural network & Support vector machine. The author has an hindex of 25, co-authored 384 publications receiving 3980 citations. Previous affiliations of Jinglu Hu include Kyushu University & Kyushu Institute of Technology.
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Journal ArticleDOI
A Graph-Based Evolutionary Algorithm: Genetic Network Programming (GNP) and Its Extension Using Reinforcement Learning
TL;DR: An extended algorithm, GNP with Reinforcement Learning (GNPRL) is proposed which combines evolution and reinforcement learning in order to create effective graph structures and obtain better results in dynamic environments.
Proceedings ArticleDOI
Image Segmentation Based on 2D Otsu Method with Histogram Analysis
Jun Zhang,Jinglu Hu +1 more
TL;DR: Two-dimensional Otsu method behaves well in segmenting images of low signal-to-noise ratio than one-dimensional (1D), but it gives satisfactory results only when the numbers of pixels in each class are close to each other.
Journal ArticleDOI
A study of evolutionary multiagent models based on symbiosis
TL;DR: Simulation results show that Masbiole can obtain various kinds of behaviors and better performances than conventional MAS in MTT by evolution, and its characteristics are examined especially with an emphasis on the behaviors of agents obtained by symbiotic evolution.
Journal ArticleDOI
A Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming
TL;DR: A new group supervisory control system for DDESs using GNP is proposed, and its optimization and performance evaluation are done through simulations, and the reduction of space requirements compared with SDESs is confirmed.
Proceedings ArticleDOI
Comparison between Genetic Network Programming (GNP) and Genetic Programming (GP)
TL;DR: A novel evolutionary method named Genetic Network Programming (GNP), whose genome is a network structure is proposed to overcome the low searching efficiency of GP and is applied to the problem of the evolution of ant behavior in order to study the effectiveness of GNP.