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» Theoretical analysis of rank-based mutation - combining expl...
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GECCO
2008
Springer
137views Optimization» more  GECCO 2008»
14 years 10 months ago
Rank based variation operators for genetic algorithms
We show how and why using genetic operators that are applied with probabilities that depend on the fitness rank of a genotype or phenotype offers a robust alternative to the Sim...
Jorge Cervantes, Christopher R. Stephens
ALIFE
2006
14 years 9 months ago
Through the Interaction of Neutral and Adaptive Mutations, Evolutionary Search Finds a Way
An evolutionary system that supports the interaction of neutral and adaptive mutations is investigated. Experimental results on a Boolean function and needle-in-haystack problems s...
Tina Yu, Julian Francis Miller
ICML
2005
IEEE
15 years 10 months ago
A theoretical analysis of Model-Based Interval Estimation
Several algorithms for learning near-optimal policies in Markov Decision Processes have been analyzed and proven efficient. Empirical results have suggested that Model-based Inter...
Alexander L. Strehl, Michael L. Littman
TSMC
2002
93views more  TSMC 2002»
14 years 9 months ago
Statistical analysis of the main parameters involved in the design of a genetic algorithm
Abstract--Most genetic algorithm (GA) users adjust the main parameters of the design of a GA (crossover and mutation probability, population size, number of generations, crossover,...
Ignacio Rojas, Jesús González, H&eac...
PKDD
2010
Springer
179views Data Mining» more  PKDD 2010»
14 years 7 months ago
Gaussian Processes for Sample Efficient Reinforcement Learning with RMAX-Like Exploration
Abstract. We present an implementation of model-based online reinforcement learning (RL) for continuous domains with deterministic transitions that is specifically designed to achi...
Tobias Jung, Peter Stone