Task Force Chair
Bing XueEvolutionary Computation Research Group, School of Engineering and Computer Science,
Victoria University of Wellington, New Zealand
Task Force Vice-Chair
Liang FengCollege of Computer Science,
Will BrowneSchool of Engineering and Computer Science,
Victoria University of Wellington,
Yew-Soon OngSchool of Computer Science and Engineering,
Nanyang Technological University,
Ivor TsangARC Future Fellow and Professor with the Centre for Quantum Computation and Intelligent Systems,
at University of Technology Sydney (UTS),
Today, it is well recognized that the processes of learning, and the transfer of what has been learned, are central to humans in problem-solving. Learning has been established to be fundamental to humans in functioning and adapting to our fast evolving society and built surroundings. Besides learning from the successes and mistakes of the past, and accordingly avoiding the same mistakes again, the ability of humans to seamlessly select and generalize relevant experiences to new problems is deemed to be most remarkable.
Within the context of computational intelligence, several core learning technologies in neural and cognitive systems, fuzzy systems, probabilistic and possibilistic reasoning, have shown promise in emulating the generalization capabilities of human learning, with many now used routinely to enhance our daily lives. Recently, in contrast to traditional machine learning approaches, Transfer Learning, which uses data from related source tasks to augment learning in a new (target) task, has attracted extensive attention and demonstrated great success in a wide range of real-world applications, including computer vision, natural language processing, speech recognition, etc.
In spite of several advances in computational intelligence, it is noted that the attempts to emulate such cognitive capabilities in problem solvers, especially those of an evolutionary nature, have received far less attention. In fact, most existing evolutionary algorithms (EAs) remain ill-equipped to exploit the potentially rich sources of knowledge that may be embedded in previous searches. With this, and the observation that any practically useful industrial system is likely to face a large number of (possibly repetitive) problems over a lifetime, it is contended that novel research advances in both the theory and application of Transfer Learning, especially for Optimization, are primed to bring about a new wave in the real-world impact of intelligent systems. The current task force thus presents an attempt to achieve this goal.
The main goal of this task force is to promote the research on crafting novel algorithm designs as well as theoretical analysis towards "intelligent" evolutionary computation, which possesses the transfer capabilities that evolve along with the problems solved. Further, this task force also aims at providing a forum for academic and industrial researchers to explore future directions of research and promote evolutionary computation techniques to a wider audience in the society of computer science and engineering.
The scope of this task force includes the following topics: