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Evolving Genetic Observers improves Simple Genetic Algorithm
Christophe Philemotte
50 Avenue Franklin Roosevelt
1050 Bruxelles


The concept of "intrinsic emergence" offers a clever way to reduce the search space cardinality and then improves the convergence to the solution. So, a second search process has to be engaged in the space of the observables and two Simple Genetic Algorithms are intertwined to solve the whole problem : one in the original space and one in the space of observables of the original one. After an intuitive application to a cellular automata, we have extended the algorithm to all optimisation problems which can be represented by bit string chromosome: observers are represented by groups of given loci where the genes take the same allele. To test its efficiency, the algorithm is applied on hard problem for genetic algorithms (GAs): hierarchichal problems and in particular Royal Road functions and Hierarchical-if-and-only-if (HIFF) function. The results are compared to those obtained with other algorithms.


Intrinsic Emergence, Genetic Algorithm, Royal Road functions, Hierarchical problems, metaheuristics


  1. Hugues Bersini. (2004) Whatever emerges should be intrinsically useful. In Artificial Life 9. The MIT Press. pp. 226--231.
  2. Christophe Philemotte and Hugues Bersini. (2005) CoEvolution of Effective Observers and Observed Multi-Agents System. In ECAL 2005 - LNAI 3630. Springer Verlag. pp. 785--749. In press.
  3. Christophe Philemotte and Hugues Bersini. (2005) Intrinsic Emergence boosts Adaptive Capacity. In GECCO'05: Proceedings of the 2005 conference on Genetic and evolutionary computation. ACM Press, New York, NY, USA. pp. 559--660.