IEEE Awards

IEEE Frank Rosenblatt Award

The IEEE Frank Rosenblatt Award is a Technical Field Award sponsored by the IEEE Computational Intelligence Society.

The award, established in 2004, is named in honor of Frank Rosenblatt, who is regarded as one of the founders of neural networks. Basing his research on study of fly vision, he developed the single-layer input layer and an output layer of neural cells. Frequent presentation of a pattern or patterns resulted in changes in the input to output connections, facilitating future recognition of these patterns, or memory. His work influenced and anticipated many modern neural network approaches.

This award will be presented for outstanding contributions to the advancement of the design, practice, techniques or theory in biologically and linguistically motivated computational paradigms including but not limited to neural networks, connectionist systems, evolutionary computation, fuzzy systems, and hybrid intelligent systems in which these paradigms are contained.

This award may be presented to an individual, multiple recipients, or a team of not more than three members. This award is administered by the Technical Field Awards Council of the IEEE Awards Board. Prize items include a bronze medal, certificate and honorarium.









Congratulate the 2020 IEEE Frank Rosenblatt Award recipient, Prof. Xin Yao.

Impacting both the foundational and practical aspects of computational intelligence, Xin Yao’s accomplishments in advancing evolutionary computation and machine learning are making it easier to solve complex optimization problems. His approaches to fast evolutionary programming, in which he proposed a widely known mutation operator and an entirely new methodology for theoretical analysis of evolutionary operators/algorithms, have been applied to neural network structure learning, optimal routing, digital filter design, and design of new materials. His work on stochastic ranking, where he introduced a novel approach to deal with constraints in evolutionary optimization by balancing objectives and penalty functions based on a new ranking method, has had a major impact on solving constraint optimization problems in the areas of electrical, chemical, mechanical, and aeronautical engineering; biology; and economics.

An IEEE Fellow, Yao is a Chair Professor of Computer Science at the Southern University of Science and Technology, Shenzhen, China, and a part-time Chair of Computer Science at the University of Birmingham, UK.