Adaptive individuals in evolving populations: models and algorithms
Adaptive individuals in evolving populations: models and algorithms
Training feedforward neural networks using genetic algorithms
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
The grounding of motivation in artificial animals: Indices of motivational behavior
Cognitive Systems Research
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In this paper, we describe adaptive processes of populations with two distinct mechanisms of evolution. Darwinian and Lamarckian. We use a simple abstract model where neural networks capable of learning are evolved through GAs. Each individual in the populations tries to maximize its life energy by learning certain rules that distinguish between two groups of materials: food and poison. The best-performing individuals are selected to reproduce offspring according to their mechanism of genetic inheritance, which is either Darwinian or Lamarckian, and the offspring conduct lifetime learning in the succeeding generation. In particular, we examine the adaptability of both populations toward a new unknown world, which is given after some evolutionary steps have taken place under the original world. As the main result, we show that only Darwinian populations can adapt to the new world.