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Adaptability of Darwinian and Lamarckian Populations toward an Unknown New World

  • Yuka Yamamoto
  • Takahiro Sasaki
  • Mario Tokoro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1674)

Abstract

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.

Keywords

Innate Error Average Fitness Artificial Life Static World Genetic Inheritance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Yuka Yamamoto
    • 1
  • Takahiro Sasaki
    • 2
  • Mario Tokoro
    • 2
  1. 1.Department of Computer Science, Faculty of Science and TechnologyKeio UniversityYokohamaJapan
  2. 2.Sony Computer Science Laboratory Inc.TokyoJapan

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