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» Fitness Clouds and Problem Hardness in Genetic Programming
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ISCAS
1999
IEEE
82views Hardware» more  ISCAS 1999»
13 years 12 months ago
Synthesis of low coefficient sensitivity digital filters using genetic programming
This paper proposes a new approach to the synthesis of low coefficient sensitivity digital filters using Genetic Programming (GP). GP is applied to the synthesis problem by establi...
K. Uesaka, M. Kawamata
GECCO
2009
Springer
103views Optimization» more  GECCO 2009»
14 years 2 months ago
Why evolution is not a good paradigm for program induction: a critique of genetic programming
We revisit the roots of Genetic Programming (i.e. Natural Evolution), and conclude that the mechanisms of the process of evolution (i.e. selection, inheritance and variation) are ...
John R. Woodward, Ruibin Bai
GECCO
2003
Springer
117views Optimization» more  GECCO 2003»
14 years 28 days ago
Improving Evolvability of Genetic Parallel Programming Using Dynamic Sample Weighting
Abstract. This paper investigates the sample weighting effect on Genetic Parallel Programming (GPP) that evolves parallel programs to solve the training samples captured directly f...
Sin Man Cheang, Kin-Hong Lee, Kwong-Sak Leung
GECCO
2003
Springer
114views Optimization» more  GECCO 2003»
14 years 28 days ago
A Linear Genetic Programming Approach to Intrusion Detection
Abstract. Page-based Linear Genetic Programming (GP) is proposed and implemented with two-layer Subset Selection to address a two-class intrusion detection classification problem a...
Dong Song, Malcolm I. Heywood, A. Nur Zincir-Heywo...
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