An evolutionary computation based constrained optimization approach for parameter tuning of an extended autoassociative memory model

摘要

We propose an evolutionary computation (EC) based constrained optimization approach for parameter tuning of an extended autoassociative memory. Being motivated to evaluate the capacity of the conventional autoassociative memory model and to go beyond the bound, we developed a series of extended models which have more parameters to increase the degree of flexibility. Meanwhile, optimization of these parameters has also become more difficult to maximize the performance of such models. By the way, we developed a new EC-based constrained optimization method in which all the constraints can be handled effectively by using the so-called “feasibilization operations” in a previous study. Now, we attempt to apply it to the optimization problem of the autoassociative memory.