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GECCO
2011
Springer
276views Optimization» more  GECCO 2011»
14 years 8 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
CORR
2010
Springer
128views Education» more  CORR 2010»
15 years 5 months ago
A Performance Study of GA and LSH in Multiprocessor Job Scheduling
Multiprocessor task scheduling is an important and computationally difficult problem. This paper proposes a comparison study of genetic algorithm and list scheduling algorithm. Bo...
S. R. Vijayalakshmi, G. Padmavathi
137
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GECCO
2007
Springer
156views Optimization» more  GECCO 2007»
15 years 11 months ago
A phenotypic analysis of GP-evolved team behaviours
This paper presents an approach to analyse the behaviours of teams of autonomous agents who work together to achieve a common goal. The agents in a team are evolved together using...
Darren Doherty, Colm O'Riordan
GECCO
2005
Springer
175views Optimization» more  GECCO 2005»
15 years 10 months ago
An approach for QoS-aware service composition based on genetic algorithms
Web services are rapidly changing the landscape of software engineering. One of the most interesting challenges introduced by web services is represented by Quality Of Service (Qo...
Gerardo Canfora, Massimiliano Di Penta, Raffaele E...
GECCO
2004
Springer
160views Optimization» more  GECCO 2004»
15 years 10 months ago
Finding Effective Software Metrics to Classify Maintainability Using a Parallel Genetic Algorithm
The ability to predict the quality of a software object can be viewed as a classification problem, where software metrics are the features and expert quality rankings the class lab...
Rodrigo A. Vivanco, Nicolino J. Pizzi