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TSMC
2002
125views more  TSMC 2002»
13 years 7 months ago
Dynamic page based crossover in linear genetic programming
Page-based Linear Genetic Programming (GP) is proposed in which individuals are described in terms of a number of pages. Pages are expressed in terms of a fixed number of instructi...
Malcolm I. Heywood, A. Nur Zincir-Heywood
GECCO
2009
Springer
164views Optimization» more  GECCO 2009»
13 years 5 months ago
Solving iterated functions using genetic programming
An iterated function f(x) is a function that when composed with itself, produces a given expression f(f(x))=g(x). Iterated functions are essential constructs in fractal theory and...
Michael D. Schmidt, Hod Lipson
GECCO
2009
Springer
109views Optimization» more  GECCO 2009»
14 years 2 months ago
Canonical representation genetic programming
Search spaces sampled by the process of Genetic Programming often consist of programs which can represent a function in many different ways. Thus, when the space is examined it i...
John R. Woodward, Ruibin Bai
GECCO
2005
Springer
151views Optimization» more  GECCO 2005»
14 years 1 months ago
Backward-chaining genetic programming
Tournament selection is the most frequently used form of selection in genetic programming (GP). Tournament selection chooses individuals uniformly at random from the population. A...
Riccardo Poli, William B. Langdon
GECCO
2009
Springer
148views Optimization» more  GECCO 2009»
13 years 5 months ago
Genetic programming for quantitative stock selection
We provide an overview of using genetic programming (GP) to model stock returns. Our models employ GP terminals (model decision variables) that are financial factors identified by...
Ying L. Becker, Una-May O'Reilly