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» Evolutionary Neuroestimation of Fitness Functions
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CEC
2010
IEEE
12 years 10 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
2004
Springer
116views Optimization» more  GECCO 2004»
14 years 4 days ago
Reducing Fitness Evaluations Using Clustering Techniques and Neural Network Ensembles
Abstract. In many real-world applications of evolutionary computation, it is essential to reduce the number of fitness evaluations. To this end, computationally efficient models c...
Yaochu Jin, Bernhard Sendhoff
EVOW
2005
Springer
14 years 8 days 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...
GECCO
2007
Springer
192views Optimization» more  GECCO 2007»
14 years 28 days ago
Estimation of fitness landscape contours in EAs
Evolutionary algorithms applied in real domain should profit from information about the local fitness function curvature. This paper presents an initial study of an evolutionary...
Petr Posík, Vojtech Franc
TCS
2008
13 years 6 months ago
Comparing evolutionary algorithms to the (1+1)-EA
In this paper, we study the conditions in which the (1+1)-EA compares favorably to other evolutionary algorithms (EAs) in terms of fitness function distribution at given iteration...
Pavel A. Borisovsky, Anton V. Eremeev