Instinct Evolution in a Goal-Seeking Neural Network

T.E. Portegys (USA)


Instincts, evolution, neural networks, goal-seeking.


Instincts are a vital part of the behavioral repertoire of organisms. Even humans rely heavily on these inborn mechanisms for survival. Many creatures, for example, build elaborate nests without ever learning through experience. This paper explores this evolutionary legacy in the context of an artificial goal-seeking neural network. An instinct is defined as a simple stimulus response sequence that is triggered by environmental and other events. The well-known “Monkey and Bananas” problem is used as a task situation. Instincts are “hard-wired” neurons in the brain of a monkey. Using a genetic algorithm, a population of monkeys evolved to successfully solve the task that none were able to solve by experience alone. The solutions were also found to be quite adaptable to variations in the task; in fact more so than a hand-crafted solution.

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