The ecology of computation
Using genetic algorithms to learn disjunctive rules from examples
Proceedings of the seventh international conference (1990) on Machine learning
Triggered Rule Discovery in Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations
The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations
Searching for diverse, cooperative populations with genetic algorithms
Evolutionary Computation
Zcs: A zeroth level classifier system
Evolutionary Computation
Implicit niching in a learning classifier system: Nature's way
Evolutionary Computation
New methods for competitive coevolution
Evolutionary Computation
Resource sharing and coevolution in evolving cellular automata
IEEE Transactions on Evolutionary Computation
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Two theoretical ecologists have recently discovered that even under the simplest models of competition, three species are sufficient to generate permanent oscillations, and five species can generate chaos (Huisman & Weissing, 2001). We can show that these results carry over into genetic algorithm (GA) resource sharing after making one minor change in the "usual" sharing methods. We also bring together previous, scattered results showing oscillatory and chaotic behavior in the "usual" GA sharing methods themselves. Thus one could argue that oscillations and chaos are fairly easy to generate once individuals are allowed to influence each other, even if such interactions are extremely simple, natural, and indirect, as they are under resource sharing. We suggest that great care be taken before assuming that any particular implementation of resource sharing leads to a unique and stable equilibrium.