Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Dimensions of communication and social organization in multi-agent robotic systems
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Evolvable 3D modeling for model-based object recognition systems
Advances in genetic programming
Advances in genetic programming
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Strongly Typed Genetic Programming in Evolving Cooperation Strategies
Proceedings of the 6th International Conference on Genetic Algorithms
Strongly typed genetic programming
Evolutionary Computation
Distributed Learning Control of Traffic Signals
Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight
Design of a Traffic Junction Controller Using Classifier Systems and Fuzzy Logic
Proceedings of the 6th International Conference on Computational Intelligence, Theory and Applications: Fuzzy Days
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Towards Self-configuration in Autonomic Electronic Institutions
Coordination, Organizations, Institutions, and Norms in Agent Systems II
Genetic Programming and Evolvable Machines
Adaptation of autonomic electronic institutions through norms and institutional agents
ESAW'06 Proceedings of the 7th international conference on Engineering societies in the agents world VII
Distributed and adaptive traffic signal control within a realistic traffic simulation
Engineering Applications of Artificial Intelligence
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Optimally controlling the timings of traffic signals within a network of intersections is a difficult but important problem. Because the traffic signals need to coordinate their behavior to achieve the common goal of optimizing traffic flow through the network, this is a problem in collective intelligence. We apply a hybrid of a genetic algorithm and strongly typed genetic programming (STGP) to the problem of learning control laws which optimize aggregate performance. STGP learns the single basic decision tree to be executed by all the intersections when deciding whether to change the phase of the traffic signal. The genetic algorithm learns different constants to be used in these decision trees for different intersections, hence allowing specialization based on differences in geometry and traffic flow. Preliminary experimental work shows that our approach yields good performance on a variety of network configurations and that it can evolve control laws which induce cooperation, communication, and specialization among the traffic signals.