Non-homogeneous Classifier Systems in a Macro-evolution Process

  • Claude Lattaud
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1813)


The synthesis of artifacts reproducing behaviors and properties of living beings is one of the main goals of Artificial Life. These artificial entities often evolve according to algorithms based on models of modern genetics. Evolutionary algorithms generally produce micro-evolution in these entities, by applying mutation and crossover on their genotype. The aim of this paper is to present Non-Homogeneous Classifier Systems, NHCS, integrating the process of macro-evolution. A NHCS is a derived type of classical Classifier Systems, CS. In a CS, all classifiers are built on the same structure and own the same properties. With a NHCS, the behavior of artificial creatures is defined by the co-evolution between several differently structured classifiers. These agents, moving in a 2D environment with obstacles and resources, must adapt themselves and breed to build viable populations. Finally, ecological niches and specific behaviors, individual and collective, appear according to initial parameters of agents and environment.


Genetic Algorithm Cellular Automaton Artificial Life Trophic Cascade Virtual Creature 
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 2000

Authors and Affiliations

  • Claude Lattaud
    • 1
  1. 1.Laboratoire d’Intelligence Artificielle de Paris5Université René DescartesParisFrance

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