On the Effectiveness of Genetic Search in Combinatorial Optimization

In this paper, we study the efficacy of genetic algorithms in the context of combinatorial optimization. In particular, we isolate the effects of cross-over, treated as the central component of genetic search. We show that for problems of nontrivial size and difficulty, the contribution of cross-over search is marginal, both synergistically when run in conjunction with mutation and selection, or when run with selection alone, the reference point being the search procedure consisting of just mutation and selection. The latter can be viewed as another manifestation of the Metropolis process. Considering the high computational cost of maintaining a population to facilitate cross-over search, its marginal benefit renders genetic search inferior to its singleton-population counterpart, the Metropolis process, and by extension, simulated annealing. This is further compounded by the fact that many problems arising in practice may inherently require a large number of state transitions for a near-optimal solution to be found, making genetic search infeasible given the high cost of computing a single iteration in the enlarged state-space.

Effectiveness of Loss labeling in Improving TCP Performance in Wired/Wireless Networks

The current congestion-oriented design of TCP hinders its ability to perform well in hybrid wireless/wired networks. We propose a new improvement on TCP NewReno (NewReno-FF) using a new loss labeling technique to discriminate wireless from congestion losses. The proposed technique is based on the estimation of average and variance of the round trip time using a filter cal led Flip Flop filter that is augmented with history information. We show the comparative performance of TCP NewReno, NewReno-FF, and TCP Westwood through extensive simulations. We study the fundamental gains and limits using TCP NewReno with varying Loss Labeling accuracy (NewReno-LL) as a benchmark. Lastly our investigation opens up important research directions. First, there is a need for a finer grained classification of losses (even within congestion and wireless losses) for TCP in heterogeneous networks. Second, it is essential to develop an appropriate control strategy for recovery after the correct classification of a packet loss.