Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Discontinuity in evolution: how different levels of organization imply preadaptation
Adaptive individuals in evolving populations
Specialization Under Social Conditions in Shared Environments
Proceedings of the Third European Conference on Advances in Artificial Life
Artificial life as a tool for biological inquiry
Artificial Life
Evolving mobile robots in simulated and real environments
Artificial Life
The grounding of motivation in artificial animals: Indices of motivational behavior
Cognitive Systems Research
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Populations of simple artificial organisms modeled as neural networks evolve a preference for one particular food type in an environment that contains more than one food type if the quantity of energy extracted from each food type is allowed to coevolve with the behavioral preference (evolvable fitness formula). If, after the emergence of the food preference, the preferred food gradually disappears from the environment at the evolutionary time scale, the evolved specialist strategy is maintained until the preferred food type has completely disappeared. Then a new specialist strategy suddenly emerges with a preference for another food type present in the environment. The appearance of the new strategy takes very few generations, in fact much fewer than in a population starting from zero (random initial population) in the same environment. This, together with the fact that the population with an evolutionary past is more efficient than the population starting from zero, suggests that the former population is preadapted to the changed environment. An analysis of the activation values of the hidden units indicates that the new food preference can be an "exaptation," that is, a new adaptation based on a structure that has previously emerged for adaptively neutral reasons.