We show that the natural evolutionary algorithm for the all-pairs shortest path problem is significantly faster with a crossover operator than without. This is the first theoretical analysis proving the usefulness of crossover for a non-artificial problem. 1

Mobile ad hoc networks are typically designed and evaluated in generic simulation environments. However the real conditions in which these networks are deployed can be quite different in terms of RF attentution, topology, and traffic load. Furthermore, specific situations often have a need for a network that is optimized along certain characteristics such as delay, energy or overhead. In response to the variety of conditions and requirements, ad hoc networking protocols are often designed with many modifiable parameters. However, there is currently no methodical way for choosing values for the parameters other than intuition and broad experience. In this paper we investigate the use of genetic algorithms for automated selection of parameters in an ad hoc networking system. We provide experimental results demonstrating that the genetic algorithm can optimize for different classes of operating conditions. We also compare our genetic algorithm optimization against hand-tuning in a complex, realistic scenario and show how the genetic algorithm provides better performance.

Selecting the routes and the assignment of link flow in a computer communication networks are extremely complex combinatorial optimization problems. Metaheuristics, such as genetic or simulated annealing algorithms, are widely applicable heuristic optimization strategies that have shown encouraging results for a large number of difficult combinatorial optimization problems. This paper considers the route selection and hence the flow assignment problem. A genetic algorithm and simulated annealing algorithm are used to solve this problem. A new hybrid algorithm combining the genetic with the simulated annealing algorithm is introduced. A modification of the genetic algorithm is also introduced. Computational experiments with sample networks are reported. The results show that the proposed modified genetic algorithm is efficient in finding good solutions of the flow assignment problem compared with other techniques.

Hybrid Peer-to-Peer (P2P) networks based on the direct connection model have two shortcomings which are high bandwidth consumption and poor semi-parallel search. However, they can further be improved by the query propagation model. In this paper, we propose a novel query routing strategy called GAroute based on the query propagation model. By giving the current P2P network topology and relevance level of each peer, GAroute returns a list of query routing paths that cover as many relevant peers as possible. We model this as the Longest Path Problem in a directed graph which is NP-complete and we obtain high quality (0.95 in 100 peers) approximate solutions in polynomial time by using Genetic Algorithm (GA). We describe the problem modeling and proposed GA for finding long paths. Finally, we summarize the experimental results which measure the scalability and quality of different searching algorithms. According to these results, GAroute works well in some large scaled P2P networks.

In this paper, the problem of dynamic quality-of-service (QoS) multicast routing in mobile ad hoc networks is investigated. Lots of interesting works have been done on multicast since it is proved to be a NP-hard problem. However, most of them consider the static network scenarios only and the multicast tree cannot adapt to the topological changes. With the advancement in communication technologies, more and more wireless mobile networks appear, e.g., mobile ad hoc networks (MANETs). In a MANET, the network topology keeps changing due to its inherent characteristics such as the node mobility and energy conservation. Therefore, an effective multicast algorithm should track the topological changes and adapt the best multicast tree to the changes accordingly. In this paper, we propose to use genetic algorithms with immigrants schemes to solve the dynamic QoS multicast problem in MANETs. MANETs are considered as target systems because they represent a new generation of wireless networks. In the construction of the dynamic network environments, two models are proposed and investigated. One is named as the general dynamics model in which the topologies are changed due to that the nodes are scheduled to sleep or wake up. The other is named as the worst dynamics model, in which the topologies are altered because some links on the current best multicast tree are removed. Extensive experiments are conducted based on both of the dynamic network models. The experimental results show that these immigrants based genetic Corresponding author.

Abstract — Whereas multicast transmission in one-to-many communications allows the operator to drastically save network resources, it also makes the routing of the traffic flows more complex then in unicast transmissions. A huge amount of possible trees have to be considered and analyzed to find the appropriate routing paths. To address this problem, we propose the use of the genetic algorithms (GA), which considerably reduce the number of solutions to be evaluated. A heuristic procedure is first used to discern a set of possible trees for each multicast session in isolation. Then, the GA are applied to find the appropriate combination of the trees to comply with the bandwidth needs of the group of multicast sessions simultaneously. The goodness of each solution is assessed by means of an expression that weights both network bandwidth allocation and one-way delay. The resulting cost function is guided by few parameters that can be easily tuned during traffic engineering operations; an appropriate setting of these parameters allows the operator to configure the desired balance between network resource utilization and provided quality of service. Simulations have been performed to compare the proposed algorithm with alternative solutions in terms of bandwidth utilization and transmission delay. Index Terms — Group multicast routing; Multicast services; Genetic Algorithms.

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Abstract—In recent years, the static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks, genetic algorithms (GAs), particle swarm optimization, etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile networks [mobile ad hoc networks (MANETs)], wireless sensor networks, etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, i.e., the network topology changes over time due to energy conservation or node mobility. Therefore, the SP routing problem in MANETs turns out to be a dynamic optimization problem. In this paper, we propose to use GAs with immigrants and memory schemes to solve the dynamic SP routing problem in MANETs. We consider MANETs as target systems because they represent new-generation wireless networks. The experimental results show that these immigrants and memory-based GAs can quickly adapt to environmental changes (i.e., the network topology changes) and produce high-quality solutions after each change. Index Terms—Dynamic optimization problem (DOP), dynamic shortest path routing problem (DSPRP), genetic algorithm (GA), immigrants scheme, memory scheme, mobile ad hoc network (MANET). I.

Cost-effective topology control is critical in wireless sensor networks. While much research has been carried out in this aspect using various methods, no attention has been made on utilizing modern heuristics for this purpose. This paper proposes a memetic algorithm-based solution for energy-aware topology control for wireless sensor networks. This algorithm (called ToCMA), using a combination of problem-specific light-weighted local search and genetic algorithm, is able to solve the minimum energy network connectivity (MENC) this NP-hard problem in an approximated manner that performs better than the classical minimum spanning tree (MST) solution. The outcomes of ToCMA can also be utilized for various network optimization and fault-tolerant purposes.

This paper offers practical design-guidelines for developing efficient genetic algorithms (GAs) to successfully solve realworld problems. As an important design component, a practical population-sizing model is presented and verified.