Trends
Interview with Cheng He, professor of computational intelligence: The ‘billions’ problem
Evolutionary algorithms (EAs) have been a popular optimisation tool for decades, showing promising performance in solving various benchmark optimisation problems. Nevertheless, using EAs on problems with over 100 decision variables (large-scale optimisation problems) remains challenging due to the “…
Headline
Evolutionary algorithms (EAs) have been a popular optimisation tool for decades, showing promising performance in solving various benchmark optimisation problems. Nevertheless, using EAs on problems with over 100 decision variables (large-scale optimisation problems) remains…
Context
Evolutionary algorithms (EAs) have been a popular optimisation tool for decades, showing promising performance in solving various benchmark optimisation problems. Nevertheless, using EAs on problems with over 100 decision variables (large-scale optimisation problems) remains challenging due to the “ curse of dimensionality “, especially for those LSOPs in real-world applications. Dr. Cheng He is a professor at Huazhong University of Science and Technology, one of China’s leading universities. His research interests are artificial/computational intelligence and its applications, and he has published more than 40 SCI papers. He is an IEEE Senior Fellow and Associate Editor of Complex and Intelligent Systems. He is a board member of PloS One and Electronics, and Chair of the IEEE CIS Intelligence Working Group. Cheng He’s research topic is Competitional Intelligence and Its Application in Power Grid.
Evidence
Pending intelligence enrichment.
Analysis
Also read: Moral and ethical discussion on artificial intelligence Also read: Can artificial intelligence achieve consciousness? That’s an interesting problem multi-objective optimisation problem is a topic in the optimisation field it means to optimise multiple conflicting objective simultaneously. let’s take an example, for designing a car, you want to be safe to be cheap but also performance very good. But is it possible? Always it’s not impossible, because you need to balance between the price and the safety and the performance. So multi-objective optimisation is trying to find the best trade off between these three conflicting objectives that’s multi-objective optimisation. Large-scale optimisation is a challenging problem in the optimisation field. Let’s take an example, if we want to design a product, usually we have only several decision variables like height, weight, and something several design variables. But consider a problem that it includes over hundreds or even thousands or billions of decision variables that’s a huge surf space you want to design this problem that would be time-consuming and always impossible. That’s light skill optimisation. That’s a challenging problem in the field of optimisation.
Key Points
- The multi-objective optimisation problem is a hot issue in the field of optimisation, which refers to the simultaneous optimisation of multiple conflicting objectives.
- The popular approach to scaling up data processing is to use parallel processing to distribute calculations across multiple processors.
Actions
Pending intelligence enrichment.
