In this paper, a mechanism of adaptive width adjustment based on immunological vaccination is proposed for the evolutionary training of RBF neural networks. Inspired by the vaccination process of the natural immune system, the algorithm implements an individual-orientated adaptation of the width in training stages to optimize the potential solutions, therefore reinforces the evolutionary capability and efficiency. A two-layer genotype-coding scheme, which enables a simultaneous evolution of network structure and parameters, is presented to achieve a compact and consistent-in-form solution. The proposed learning strategy is tested on several benchmark problems and results demonstrate promise.
A component selection is a crucial problem in Component-Based Software Engineering (CBSE), which is concerned with the assembly of pre-existing software components.
We are approaching the component selection involving dependencies between components. We formulate the problem as multiobjective, involving two objectives and one constraint. The approach used is an evolutionary computation technique. The experiments and comparisons with the greedy approach show the effectiveness of the proposed approach.