Evolving Neural Networks for Prediction with Negative Correlation Search: Application in Consumer Demand Forecasting

Zhu Yichen; Chen Yang; Ghandar, Adam

10.1109/SSCI47803.2020.9308565

2020

IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

978-1-7281-2548-0

2020 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI)

384-391

DEC 01-04, 2020

null,null,ELECTR NETWORK

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Neuroevolution is a powerful approach for learning neural networks for a large variety of machine learning applications. This paper describes a new approach that uses Negatively Correlated Search (NCS) to extend the basic NEAT neur. This approach uses NCS as a means for learning a population of negatively correlated neural network solutions, meaning that the individuals in the population are suited for performing well in different kinds of problem cases (or examples). An empirical evaluation of the proposed methodology we term NCS-NEAT leads to improved performance over basic NEAT in terms of accuracy and problem specific criteria and is a promising way to scale neuroevolution up to handle larger datasets without placing restrictions on the topology neural networks that are able to be learned. Two different machine learning problem classes were selected to he representative of a broad range of applications of deep learning and neuroevolution: the first is a price forecasting problem to predict the prices of mobile phones based on their characteristics; the second is a reinforcement learning test problem to validate the novel approach in a different type of problem.

Machine learning Neuro-Evolution NCS

第一 ; 通讯

英语

CPCI-S ; EI

Computer Science ; Engineering

Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Electrical & Electronic

WOS:000682772900051

20210409827649

Deep learning ; Forecasting ; Intelligent computing ; Learning systems ; Reinforcement learning

Artificial Intelligence:723.4

Web of Science

Southern Univ Sci & Technol SUSTech, Comp Sci & Engn, Shenzhen, Peoples R China

Zhu Yichen,Chen Yang,Ghandar, Adam. Evolving Neural Networks for Prediction with Negative Correlation Search: Application in Consumer Demand Forecasting[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2020:384-391.