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)


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.


Innate Error Average Fitness Artificial Life Static World Genetic Inheritance 
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  1. 1.
    David H. Ackley and Michael L. Littman. Interactions between Learning and Evolution. In Artificial Life II, SFI Studies in the Sciences of Complexity, vol. X, pages 487–509. Addison-Wesley, 1992.Google Scholar
  2. 2.
    David H. Ackley and Michael L. Littman. A Case for Lamarckian Evolution. In Artificial Life III, SFI Studies in the Sciences of Complexity, vol. XVII, pages 3–10. Addison-Wesley, 1994.Google Scholar
  3. 3.
    J. Mark Baldwin. A new factor in evolution. American Naturalist, 30:441–451, 1896.CrossRefGoogle Scholar
  4. 4.
    Richard K. Belew and Melanie Mitchell. Adaptive Individuals in Evolving Populations: Models and Algorithms. Addison-Wesley, 1996.Google Scholar
  5. 5.
    John J. Grefenstette. Lamarckian Learning in Multi-agent Environments. In Proceedings of 4th International Conference on Genetic Algorithms and their applications (ICGA-91), pages 303–310, 1991.Google Scholar
  6. 6.
    G. E. Hinton and S. J. Nowlan. How Learning Can Guide Evolution. Complex Systems, 1:495–502, 1987.zbMATHGoogle Scholar
  7. 7.
    Akira Imada and Keijiro Araki. Lamarckian evolution of associative memory. In Proceedings of 1996 IEEE The Third International Conference on Evolutionary Computation (ICEC-96), pages 676–680, 1996.Google Scholar
  8. 8.
    David J. Montana and Lawrence Davis. Training Feedforward Neural Networks Using Genetic Algorithms. In Proceedings of the 11th International Conference on Artificial Intelligence (IJCAI-89), pages 762–767, 1989.Google Scholar
  9. 9.
    Domenico Parisi, Stefano Nolfi, and Federico Cecconi. Learning, Behavior and Evolution. In Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, pages 207–216, 1991.Google Scholar
  10. 10.
    Takahiro Sasaki and Mario Tokoro. Adaptation toward changing environments: Why darwinian in nature? In 4th European Conference on Artificial Life (ECAL-97), pages 145–153, 1997.Google Scholar
  11. 11.
    Takahiro Sasaki and Mario Tokoro. Adaptation under changing environments with various rates of inheritance of acquired characters: Comparison between darwinianm and lamarckian evolution. In 2nd Asia-Pacific Conference on Simulated Evo lution And Learning (SEAL-98), 1998.Google Scholar

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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