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GECCO
2011
Springer
276views Optimization» more  GECCO 2011»
13 years 1 months ago
Evolution of reward functions for reinforcement learning
The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...
Scott Niekum, Lee Spector, Andrew G. Barto
CISIS
2008
IEEE
14 years 4 months ago
Hardware Software Partitioning Problem in Embedded System Design Using Particle Swarm Optimization Algorithm
Hardware/software partitioning is a crucial problem in embedded system design. In this paper, we provide an alternative approach to solve this problem using Particle Swarm Optimiz...
Alakananda Bhattacharya, Amit Konar, Swagatam Das,...
DATE
2007
IEEE
98views Hardware» more  DATE 2007»
14 years 4 months ago
Simulation-based reusable posynomial models for MOS transistor parameters
We present an algorithm to automatically design posynomial models for parameters of the MOS transistors using simulation data. These models improve the accuracy of the Geometric P...
Varun Aggarwal, Una-May O'Reilly
GECCO
2007
Springer
177views Optimization» more  GECCO 2007»
14 years 4 months ago
On the behavioral diversity of random programs
Generating a random sampling of program trees with specified function and terminal sets is the initial step of many program evolution systems. I present a theoretical and experim...
Moshe Looks
CEC
2010
IEEE
13 years 1 months ago
Tweaking a tower of blocks leads to a TMBL: Pursuing long term fitness growth in program evolution
— If a population of programs evolved not for a few hundred generations but for a few hundred thousand or more, could it generate more interesting behaviours and tackle more comp...
Tony E. Lewis, George D. Magoulas