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» Performance comparison of memetic algorithms
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140
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GECCO
2008
Springer
128views Optimization» more  GECCO 2008»
15 years 4 months ago
Adapted Pittsburgh classifier system: building accurate strategies in non markovian environments
This paper focuses on the study of the behavior of a genetic algorithm based classifier system, the Adapted Pittsburgh Classifier System (A.P.C.S), on maze type environments con...
Gilles Énée, Mathias Péroumal...
IOR
2006
163views more  IOR 2006»
15 years 3 months ago
Adaptive Importance Sampling Technique for Markov Chains Using Stochastic Approximation
For a discrete-time finite-state Markov chain, we develop an adaptive importance sampling scheme to estimate the expected total cost before hitting a set of terminal states. This s...
T. P. I. Ahamed, Vivek S. Borkar, S. Juneja
121
Voted
GECCO
2009
Springer
141views Optimization» more  GECCO 2009»
15 years 10 months ago
Distributed hyper-heuristics for real parameter optimization
Hyper-heuristics (HHs) are heuristics that work with an arbitrary set of search operators or algorithms and combine these algorithms adaptively to achieve a better performance tha...
Marco Biazzini, Balázs Bánhelyi, Alb...
ROBOCUP
2007
Springer
153views Robotics» more  ROBOCUP 2007»
15 years 10 months ago
Model-Based Reinforcement Learning in a Complex Domain
Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the en...
Shivaram Kalyanakrishnan, Peter Stone, Yaxin Liu
IDEAS
2006
IEEE
109views Database» more  IDEAS 2006»
15 years 9 months ago
Collaborative Filtering Process in a Whole New Light
Collaborative Filtering (CF) Systems are gaining widespread acceptance in recommender systems and ecommerce applications. These systems combine information retrieval and data mini...
Panagiotis Symeonidis, Alexandros Nanopoulos, Apos...