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97
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CEC
2007
IEEE
15 years 10 months ago
Implicit alternative splicing for genetic algorithms
— In this paper we present a new nature-inspired variation operator for binary encodings in genetic algorithms (GAs). Our method, called implicit alternative splicing (iAS), is r...
Philipp Rohlfshagen, John A. Bullinaria
217
Voted
ASPDAC
2006
ACM
129views Hardware» more  ASPDAC 2006»
15 years 9 months ago
Yield-area optimizations of digital circuits using non-dominated sorting genetic algorithm (YOGA)
With shrinking technology, the timing variation of a digital circuit is becoming the most important factor while designing a functionally reliable circuit. Gate sizing has emerged...
Vineet Agarwal, Janet Meiling Wang
GECCO
2005
Springer
152views Optimization» more  GECCO 2005»
15 years 9 months ago
GAMM: genetic algorithms with meta-models for vision
Recent adaptive image interpretation systems can reach optimal performance for a given domain via machine learning, without human intervention. The policies are learned over an ex...
Greg Lee, Vadim Bulitko
125
Voted
IAT
2003
IEEE
15 years 9 months ago
Integrating Reinforcement Learning, Bidding and Genetic Algorithms
This paper presents a multi-agent reinforcement learning bidding approach (MARLBS) for performing multi-agent reinforcement learning. MARLBS integrates reinforcement learning, bid...
Dehu Qi, Ron Sun
97
Voted
GECCO
2007
Springer
124views Optimization» more  GECCO 2007»
15 years 7 months ago
Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms
The Negative Slope Coefficient (nsc) is an empirical measure of problem hardness based on the analysis of offspring-fitness vs. parent-fitness scatterplots. The nsc has been teste...
Riccardo Poli, Leonardo Vanneschi