Automatic design of software architecture by use of genetic algorithms has already been shown to be feasible. A natural problem is to augment – if not replace – genetic algori...
Stock selection for hedge fund portfolios is a challenging problem for Genetic Programming (GP) because the markets (the environment in which the GP solution must survive) are dyn...
Power consumption and portability issues are becoming increasingly significant in embedded system architectures. Therefore, it is important that chip architects and integrated circ...
Genetic Programming offers freedom in the definition of the cost function that is unparalleled among supervised learning algorithms. However, this freedom goes largely unexploited...
This paper describes the development of an evolutionary algorithm called Multipopulation Cooperative Coevolutionary Programming (MCCP) that extends Genetic Programming (GP) to sea...