An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Genetic Algorithm for Mobile Robot Localization Using Ultrasonic Sensors
Journal of Intelligent and Robotic Systems
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
A QoS Routing Method for Ad-Hoc Networks Based on Genetic Algorithm
DEXA '03 Proceedings of the 14th International Workshop on Database and Expert Systems Applications
Neural Computing and Applications
Optimizing parameters of a mobile ad hoc network protocol with a genetic algorithm
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
A genetic algorithm for shortest path routing problem and the sizing of populations
IEEE Transactions on Evolutionary Computation
Convergence analysis of genetic algorithms for topology control in MANETs
Sarnoff'10 Proceedings of the 33rd IEEE conference on Sarnoff
Self spreading nodes using potential games and genetic algorithms
Sarnoff'10 Proceedings of the 33rd IEEE conference on Sarnoff
Efficient node distribution techniques in mobile ad hoc networks using game theory
MILCOM'09 Proceedings of the 28th IEEE conference on Military communications
Markov chain models for genetic algorithm based topology control in MANETs
EvoCOMNET'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part II
Self organization for area coverage maximization and energy conservation in mobile ad hoc networks
Transactions on Computational Science XV
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We present different approaches for knowledge sharing bio-inspired mobile agents to obtain a uniform distribution of the nodes over a geographical terrain. In this application, the knowledge sharing agents in a mobile ad hoc network adjust their speed and directions based on genetic algorithms (GAs). With an analytical model, we show that the best fitness value is obtained when the number of neighbors for a mobile agent is equal to the mean node degree. The genetic information that each mobile agent exchanges with other neighboring agents within its communication range includes the node's location, speed, and movement direction. We have implemented a simulation software to study the effectiveness of different GA-based algorithms for network performance metrics including node densities, speed, and number of generations that a GA runs. Compared to random-walk and Hill Climbing approaches, all GA-based cases show encouraging results by converging towards a uniform node distribution.