Mobile Ad Hoc Networks (MANETs) are a class of infrastructure less network architecture which are formed by a collection of mobile nodes that communicate with each other using multi-hop wireless links. They eliminate the need for central management, hence each node must operate cooperatively to successfully maintain the network. Each node performs as a source, a sink and a router. Future applications of MANETs are expected to be based on all-IP architecture, carrying a multitude of real-time multimedia applications such as voice, video and data. It would be necessary for MANETs to have an efficient routing and quality of service (QoS) mechanism to support diverse applications. This thesis proposes a set of cooperative protocols that provide support for QoS routing. The first is the on-demand, Non-Disjoint Multiple Routes Discovery protocol (NDMRD). NDMRD allows the establishment of multiple paths with node non-disjoint between source and destination node. It returns to the source a collection of routes with the QoS parameters. The second part of the protocol is the Node State Monitoring protocol for the purpose of monitoring, acquisition, dissemination and accumulation of QoS route information. The third part of the protocol implements the QoS route selection based on a Genetic Algorithm. The GA is implemented online with predetermined initial population and weighted-sum fitness function which operates simultaneously on the node bandwidth, media access delay, end to end delay and the node connectivity index (NCI). The term node connectivity index is a numerical value designed to predict comparatively the longest time a node-pair might be connected wirelessly.

This thesis deals with analysis of genetic algorithms. It is focused on various approaches to creation of new populations. A comparison between basic principles of operation of genetic algorithms and processes occurring in living organisms is drawn here. Some methods of application of particular steps of genetic algorithms are introduced and a suitability of the methods to certain types of problems is considered. The main goal in the thesis is to apply genetic algorithms in solving three types of optimization problems, namely the solution of functions with a single major extreme, functions with flat (slight) extreme and also functions with many local extremes.