@inproceedings{be53c6bed0114af088cbcb9372a8f6ce,
title = "Evolutionary hexapod robot gait control using a new recurrent neural network learned through group-based hybrid metaheuristic algorithm",
abstract = "This paper proposes a new recurrent neural network (RNN) structure evolved to control the gait of a hexapod robot for fast forward walking. In this evolutionary robot, the gait control problem is formulated as an optimization problem with the objective of a fast forward walking speed and a small deviation in the forward walking direction. Evolutionary optimization of the RNNs through a group-based hybrid metaheuristic algorithm is proposed to find the optimal RNN controller. Preliminary simulation results with comparisons show the advantage of the proposed approach1.",
keywords = "Evolutionary robots, Genetic algorithms, Hexapod robots, Particle swarm optimization",
author = "Juang, {Chia Feng} and Chang, {Yu Cheng} and Chung, {I. Fang}",
note = "Publisher Copyright: {\textcopyright} 2018 Copyright is held by the owner/author(s).; null ; Conference date: 15-07-2018 Through 19-07-2018",
year = "2018",
month = jul,
day = "6",
doi = "10.1145/3205651.3205671",
language = "English",
series = "GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion",
publisher = "Association for Computing Machinery, Inc",
pages = "111--112",
booktitle = "GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion",
}