Genetic Algorithm (GA) and other Evolutionary Algorithms (EAs) have been successfully applied to solve constrained minimum spanning tree (MST) problems of the communication network design and also have been used extensively in a wide variety of communication network design problems. Choosing an appropriate representation of candidate solutions to the problem is the essential issue for applying GAs to solve real world network design problems, since the encoding and the interaction of the encoding with the crossover and mutation operators have strongly influence on the success of GAs. In this paper, we investigate a new encoding crossover and mutation operators on the performance of GAs to design of minimum spanning tree problem. Based on the performance analysis of these encoding methods in GAs, we improve predecessor-based encoding, in which initialization depends on an underlying random spanning-tree algorithm. The proposed crossover and mutation operators offer locality, heritability, and computational efficiency. We compare with the approach to others that encode candidate spanning trees via the Pr?fer number-based encoding, edge set-based encoding, and demonstrate better results on larger instances for the communication spanning tree design problems.

By close analogy of annealing for solids, we devise a new algorithm, called APS, for the time evolution of both the state and the synapses of the Hopfield's neural network. Through constrainedly random perturbation of the synapses of the network, the evolution of the state will ignore the tremendous number of small minima and reach a good minimum. The synapses resemble the microstructure of a network. This new algorithm anneals the microstructure of the network through a thermal controlled process. And the algorithm allows us to obtain a good minimum of the Hopfield's model efficiently. We show the potential of this approach for optimization problems by applying it to the will-known traveling salesman problem. The performance of this new algorithm has been supported by many computer simulations.

Bit-interleaved coded modulation with iterative decoding (BICM-ID) is suitable for correlated Rayleigh fading channels. Additionally, BICM-ID using differential encoding can avoid the pilot overhead. In this paper, we consider BICM-ID using 16-DAPSK (differential amplitude and phase-shift keying). We first derive the probability of receiving signals conditioned on the transmission of input bits for general differential encoding; then we propose two new 16-DAPSK bit labeling methods. In addition, convolutional codes for the new bit labeling are developed. Both the minimum distance and the simulation results show that the proposed labeling has better error performance than that of the original differential encoding, and the searched new codes can further improve the error performance.