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» Theoretical analysis of rank-based mutation - combining expl...
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GECCO
2008
Springer
137views Optimization» more  GECCO 2008»
13 years 6 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
13 years 5 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
14 years 5 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»
13 years 4 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»
13 years 2 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