ACM Computing Surveys (CSUR)
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Evolving Self-Organizing Behaviors for a Swarm-Bot
Autonomous Robots
Genetic algorithms, selection schemes, and the varying effects of noise
Evolutionary Computation
Positional communication and private information in honeybee foraging models
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Modeling and Optimization of Adaptive Foraging in Swarm Robotic Systems
International Journal of Robotics Research
Automatic tuning of agent-based models using genetic algorithms
MABS'05 Proceedings of the 6th international conference on Multi-Agent-Based Simulation
Formica ex machina: ant swarm foraging from physical to virtual and back again
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
An evolutionary approach for robust adaptation of robot behavior to sensor error
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Collective foraging is a canonical problem in the study of social insect behavior, as well as in biologically inspired engineered systems. Pheromone recruitment is a well-studied mechanism by which ants coordinate their foraging. Another mechanism for information use is the memory of individual ants, which allows an ant to return to a site it has previously visited. There is synergy in the use of social and private information: ants with poor private information can follow pheromone trails; while ants with private information can ignore trails and instead rely on memory. We developed an agent-based model of foraging by harvester ants, and optimized the model to maximize foraging rate using genetic algorithms. We found that ants' individual memory provided greater benefit in terms of increased foraging rate than pheromone trails in a variety of food distributions. When the two strategies are used together, they out-perform either strategy alone. We compare the behavior of these models to observations of harvester ants in the field. We discuss why individual memory is more beneficial in this system than pheromone trails. We suggest that individual memory may be an important addition to ant colony optimization and swarm robotics systems, and that genetic algorithms may be useful in finding an adaptive balance between individual foraging based on memory and recruitment based on communication.