Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolving collective behavior in an artificial ecology
Artificial Life
An Empirical Comparison of Particle Swarm and Predator Prey Optimisation
AICS '02 Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science
Analysis of the publications on the applications of particle swarm optimisation
Journal of Artificial Evolution and Applications - Regular issue
Emergence of collective behavior in evolving populations of flying agents
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A honey bees mating optimization algorithm for the open vehicle routing problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Swarm robotics: from sources of inspiration to domains of application
SAB'04 Proceedings of the 2004 international conference on Swarm Robotics
Gaussian mixture modeling for dynamic particle swarm optimization of recurrent problems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Multi-objective particle swarm optimisation (PSO) for feature selection
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Animal grouping behaviors have been widely studied due to their implications for understanding social intelligence, collective cognition, and potential applications in engineering, artificial intelligence, and robotics. An important biological aspect of these studies is discerning which selection pressures favor the evolution of grouping behavior. The selfish herd hypothesis states that concentrated groups arise because prey selfishly attempt to place their conspecifics between themselves and the predator, thus causing an endless cycle of movement toward the center of the group. Using an evolutionary model of a predator-prey system, we show that the predator attack mode plays a critical role in the evolution of the selfish herd. Following this discovery, we show that density-dependent predation provides an abstraction of Hamilton's original formulation of "domains of danger." Finally, we verify that density-dependent predation provides a sufficient selective advantage for prey to evolve the selfish herd in response to predation by coevolving predators. Thus, our work verifies Hamilton's selfish herd hypothesis in a digital evolutionary model, refines the assumptions of the selfish herd hypothesis, and generalizes the domain of danger concept to density-dependent predation.