TY  - CONF
AU  - Martinez, Aritz D.
AU  - Osaba, Eneko
AU  - Sery, Javier Del
AU  - Herrera, Francisco
PY  - 2020
DO  - 10.1109/CEC48606.2020.9185667
SN  - 9781728169293
UR  - https://hdl.handle.net/11556/2489
AB  - In recent years, Multifactorial optimization (MFO) has gained a notable momentum in the research community. MFO is known for its inherent capability to efficiently address multiple optimization tasks at the same time, while transferring information...
LA  - eng
PB  - Institute of Electrical and Electronics Engineers Inc.
TI  - Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial optimization
TY  - conference output
ER  -