Hierarchical Evolution Of Neural Networks (1998)
In most applications of neuro-evolution, each individual in the population represents a complete neural network. Recent work on the SANE system, however, has demonstrated that evolving individual neurons often produces a more efficient genetic search. This paper demonstrates that while SANE can solve easy tasks very quickly, it often stalls in larger problems. A hierarchical approach to neuro-evolution is presented that overcomes SANE's difficulties by integrating both a neuron-level exploratory search and a network-level exploitive search. In a robot arm manipulation task, the hierarchical approach outperforms both a neuron-based search and a network-based search.
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In Proceedings of the 1998 IEEE Conference on Evolutionary Computation (ICEC98), pp. 428-433, Anchorage, AK 1998. Piscataway, NJ: IEEE.
Bibtex:

Risto Miikkulainen Faculty risto [at] cs utexas edu
David E. Moriarty Ph.D. Alumni moriarty [at] alumni utexas net