Co-evolutionary learning of neural networks

Compared with the conventional approaches, the evolutionary algorithms (EAs) are more efficient for system design in the sense that EAs can provide higher opportunity for obtaining the global optimal solution. However, in most existing EAs, an individual corresponds directly to a possible solution, and a large amount of computations is required for designing large-scaled systems. To solve this problem, this paper proposes a co-evolutionary algorithm (CEA). The basic idea is to divide and conquer: divide the system into many small homogeneous modules, define an individual as a module, find many good individuals using existing EAs, and put them together again to form the whole system. To make the study more concrete, we focus the discussion on the evolutionary learning of neural networks for pattern recognition. Experimental results are provided to show the procedure and the performance of the CEA.