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» Potential fitness for genetic programming
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CEC
2010
IEEE
12 years 11 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
GECCO
2010
Springer
191views Optimization» more  GECCO 2010»
13 years 7 months ago
Fitness importance for online evolution
To complement standard fitness functions, we propose "Fitness Importance" (FI) as a novel meta-heuristic for online learning systems. We define FI and show how it can be...
Philip Valencia, Raja Jurdak, Peter Lindsay
IWMM
2000
Springer
144views Hardware» more  IWMM 2000»
13 years 11 months ago
Memory Allocation with Lazy Fits
Dynamic memory allocation is an important part of modern programming languages. It is important that it be done fast without wasting too much memory. Memory allocation using lazy ...
Yoo C. Chung, Soo-Mook Moon
GECCO
2006
Springer
150views Optimization» more  GECCO 2006»
13 years 11 months ago
Nonlinear parametric regression in genetic programming
Genetic programming has been considered a promising approach for function approximation since it is possible to optimize both the functional form and the coefficients. However, it...
Yung-Keun Kwon, Sung-Soon Choi, Byung Ro Moon
FLAIRS
2006
13 years 9 months ago
Improving Modularity in Genetic Programming Using Graph-Based Data Mining
We propose to improve the efficiency of genetic programming, a method to automatically evolve computer programs. We use graph-based data mining to identify common aspects of highl...
Istvan Jonyer, Akiko Himes