A further generalization of the finite-population geiringer-like theorem for pomdps to allow recombination overarbitrary set covers

Authors:
Boris S. Mitavskiy;Jun He
Affiliations:
Aberystwyth University, Aberystwyth, United Kingdom;Aberystwyth University, Aberystwyth, United Kingdom
Venue:
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
Year:
2013

Citing 8
Cited 1

Theory of genetic algorithms

Theoretical Computer Science
The Simple Genetic Algorithm: Foundations and Theory

The Simple Genetic Algorithm: Foundations and Theory
A New Interpretation of Schema Notation that Overtums the Binary Encoding Constraint

Proceedings of the 3rd International Conference on Genetic Algorithms
Theory of genetic algorithms II: models for genetic operators over the string-tensor representation of populations and convergence to global optima for arbitrary fitness function under scaling

Theoretical Computer Science
Schema theory for genetic programming with one-point crossover and point mutation

Evolutionary Computation
Exploiting symmetries in POMDPs for point-based algorithms

AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
A schema-based version of geiringer's theorem for nonlinear genetic programming with homologous crossover

FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
An extension of geiringer's theorem for a wide class of evolutionary search algorithms

Evolutionary Computation

Geiringer theorems: from population genetics to computational intelligence, memory evolutive systems and Hebbian learning

Natural Computing: an international journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

A popular current research trend deals with expanding the Monte-Carlo tree search sampling methodologies to the environments with uncertainty and incomplete information. Recently a finite population version of Geiringer theorem with nonhomologous recombination has been adopted to the setting of Monte-Carlo tree search to cope with randomness and incomplete information by exploiting the entrinsic similarities within the state space of the problem. The only limitation of the new theorem is that the similarity relation was assumed to be an equivalence relation on the set of states. In the current paper we lift this "curtain of limitation" by allowing the similarity relation to be modeled in terms of an arbitrary set cover of the set of state-action pairs.