<p>Route planning is an important part of road network. To select an optimized route several factors such as flow of traffic, speed limits of road. are concerned. Total cost of such a network depends on the number of junctions between the source and the destination. Due to the growth of the nodes in the network it becomes a tough job to determine the exact path using deterministic algorithms so in such cases genetic algorithms (GA) plays a vital role to find the optimized route. Crossover is an important operator ingenetic algorithm. The efficiency of thegenetic algorithmis directlyinfluenced by the time of a crossover operation. In this paper a new crossoveroperator closest-node pairing crossover (CNPC) is recommended which is explicitly designed to improve the performance of the genetic algorithm compared to other well-known crossover operators such as point based crossover and order crossover. The distance aspect of the network problem has been exploited in this crossover operator. This proposed technique gives a better result compared to the other crossover operator with the fitness value of 0.0048. The CNPC operator gives better rate of convergence compared to the other crossover operators.</p>

Shortest path problem has emerged to be one of the significant areas of research and there are various algorithms involved in it. One of the successful optimization techniques is genetic algorithm (GA). This paper proposes an efficient hybrid genetic algorithm where initially we use a map reduction technique to the graph and then find the shortest path using the conventional genetic algorithm with an improved crossover operator. On comparing this hybrid algorithm with other algorithms, it has been detected that the performance of the modified genetic algorithm is better as comparison to the other methods in terms of various metrics used for the evaluation.

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Route planning has an important role in navigation systems. In order to select an optimized route the traveller has to take various factors into consideration. Traffic congestion is an important factor which needs to be considered while route planning. As the numbers of vehicles are increasing on the road the traffic congestion is also increasing in an exponential manner. In a congested area the best approach to search for a route is to select an alternative path so that we can reach our destination and indirectly save some time. In the recent years route planning system has become an important area of research since the number of vehicles are increasing day-by-day but the traffic structure is un-expandable. In this paper a genetic algorithm is proposed to develop an alternate route which results in smooth flow of traffic. Genetic Algorithm’s main aim is to create an optimized path.

Genetic algorithm uses the natural selection process for any search process. It is an optimization process where integration among different vital parameters like crossover and mutation plays a major role. The parameters have an impact on the algorithm by their probabilities. In this paper we would review the different strategies used for the selection of crossover and mutation ratios and suggest a dynamic approach for modifying the ratios during runtime. We start with a mutation ratio 0% and crossover ratio 100% where the mutation ratio slowly increases and the crossover ratio decreases (MICD). The final mutation ratio will be 0% and crossover ratio will be 100% at the end of the search process. We also do the reverse process of considering the mutation ratio to be maximum and crossover ratio to be minimum and slowly decrease the mutation ratio and increase the crossover ratio (MDCI). We compare the proposed method with two pre-existing parameter tuning methods and found that this dynamic approach of incrementing the mutation and decrementing the crossover value was more effective when the size of the population was large.