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- Title
Geiringer theorems: from population genetics to computational intelligence, memory evolutive systems and Hebbian learning.
- Authors
Mitavskiy, Boris S.; Tuci, Elio; Cannings, Chris; Rowe, Jonathan; He, Jun
- Abstract
The classical Geiringer theorem addresses the limiting frequency of occurrence of various alleles after repeated application of crossover. It has been adopted to the setting of evolutionary algorithms and, a lot more recently, reinforcement learning and Monte-Carlo tree search methodology to cope with a rather challenging question of action evaluation at the chance nodes. The theorem motivates novel dynamic parallel algorithms that are explicitly described in the current paper for the first time. The algorithms involve independent agents traversing a dynamically constructed directed graph that possibly has loops and multiple edges. A rather elegant and profound category-theoretic model of cognition in biological neural networks developed by a well-known French mathematician, professor Andree Ehresmann jointly with a neurosurgeon, Jan Paul Vanbremeersch over the last thirty years provides a hint at the connection between such algorithms and Hebbian learning.
- Subjects
POPULATION genetics; POPULATION biology; PLANT population genetics; COMPUTATIONAL intelligence; EVOLUTIONARY algorithms; GENETIC algorithms
- Publication
Natural Computing, 2013, Vol 12, Issue 4, p473
- ISSN
1567-7818
- Publication type
Article
- DOI
10.1007/s11047-013-9395-4