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» Memory Based on Abstraction for Dynamic Fitness Functions
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CDC
2010
IEEE
160views Control Systems» more  CDC 2010»
12 years 11 months ago
Adaptive bases for Q-learning
Abstract-- We consider reinforcement learning, and in particular, the Q-learning algorithm in large state and action spaces. In order to cope with the size of the spaces, a functio...
Dotan Di Castro, Shie Mannor
EVOW
2005
Springer
13 years 10 months ago
Developing Fitness Functions for Pleasant Music: Zipf's Law and Interactive Evolution Systems
Abstract. In domains such as music and visual art, where the quality of an individual often depends on subjective or hard to express concepts, the automating fitness assignment bec...
Bill Z. Manaris, Penousal Machado, Clayton McCaule...
CEC
2007
IEEE
13 years 11 months ago
Non-separable fitness functions for evolutionary shape optimization benchmarking
—Target shape matching can be used as a quick and easy surrogate task when evaluating optimization algorithms intended for computationally expensive tasks, such as turbine blade ...
Tim A. Yates, Thorsten Schnier
DAGM
2007
Springer
13 years 11 months ago
Learning Robust Objective Functions with Application to Face Model Fitting
Abstract. Model-based image interpretation extracts high-level information from images using a priori knowledge about the object of interest. The computational challenge is to dete...
Matthias Wimmer, Sylvia Pietzsch, Freek Stulp, Ber...
ICDE
1999
IEEE
150views Database» more  ICDE 1999»
14 years 6 months ago
Managing Distributed Memory to Meet Multiclass Workload Response Time Goals
In this paper we present an online method for managing a goaloriented buffer partitioning in the distributed memory of a network of workstations. Our algorithm implements a feedba...
Arnd Christian König, Markus Sinnwell