Sciweavers

12 search results - page 1 / 3
» Implicit fitness and heterogeneous preferences in the geneti...
Sort
View
GECCO
2010
Springer
244views Optimization» more  GECCO 2010»
13 years 4 months ago
Implicit fitness and heterogeneous preferences in the genetic algorithm
This paper takes an economic approach to derive an evolutionary learning model based entirely on the endogenous employment of genetic operators in the service of self-interested a...
Justin T. H. Smith
ICIP
2009
IEEE
13 years 2 months ago
Genetic algorithms for 3d reconstruction with supershapes
Supershape model is a recent primitive that represents numerous 3D shapes with several symmetry axes. The main interest of this model is its capability to reconstruct more complex...
Sophie Voisin, Mongi A. Abidi, Sebti Foufou, Fr&ea...
GECCO
2007
Springer
187views Optimization» more  GECCO 2007»
13 years 10 months ago
Defining implicit objective functions for design problems
In many design tasks it is difficult to explicitly define an objective function. This paper uses machine learning to derive an objective in a feature space based on selected examp...
Sean Hanna
GECCO
2007
Springer
184views Optimization» more  GECCO 2007»
13 years 8 months ago
Multi-objective optimization tool for a free structure analog circuits design using genetic algorithms and incorporating parasit
This paper presents a novel approach for a free structure analog circuit design using Genetic Algorithms (GA). A major problem in a free structure circuit is its sensitivity calcu...
Yaser M. A. Khalifa, Badar K. Khan, Faisal Taha
PPSN
1998
Springer
13 years 8 months ago
On Genetic Algorithms and Lindenmayer Systems
This paper describes a system for simulating the evolution of artificial 2D plant morphologies. Virtual plant genotypes are inspired by the mathematical formalism known as Lindenma...
Gabriela Ochoa