GECCO
2008
Springer
13 years 6 months ago
2008
Springer
We describe the application of genetic programming (GP) to a problem in pure mathematics, in the study of finite algebras. We document the production of human-competitive results...
GECCO
2008
Springer
13 years 6 months ago
2008
Springer
The hurdles in solving Constrained Optimization Problems (COP) arise from the challenge of searching a huge variable space in order to locate feasible points with acceptable solut...
GECCO
2008
Springer
13 years 6 months ago
2008
Springer
This paper presents a new Genetic Algorithm for Protein Structure Prediction problem in both 2D and 3D hydrophobichydrophilic lattice models, introduced in [1]. Our algorithm evol...
GECCO
2008
Springer
13 years 6 months ago
2008
Springer
This paper presents a theoretical definition for designing EDAs called Elitist Convergent Estimation of Distribution Algorithm (ECEDA), and a practical implementation: the Boltzm...
GECCO
2008
Springer
13 years 6 months ago
2008
Springer
This paper merges hierarchical reinforcement learning (HRL) with ant colony optimization (ACO) to produce a HRL ACO algorithm capable of generating solutions for large domains. Th...
GECCO
2008
Springer
13 years 6 months ago
2008
Springer
We propose an approach of automated co-evolution of the optimal values of attributes of active sensing (orientation, range and timing of activation of sensors) and the control of ...
GECCO
2008
Springer
13 years 6 months ago
2008
Springer
This paper addresses the image registration problem applying genetic algorithms. The image registration’s objective is the definition of a mapping that best match two set of poi...
GECCO
2008
Springer
13 years 6 months ago
2008
Springer
GECCO
2008
Springer
13 years 6 months ago
2008
Springer
Single and multi-step time-series predictors were evolved for forecasting minimum bidding prices in a simulated supply chain management scenario. Evolved programs were allowed to ...
GECCO
2008
Springer
13 years 6 months ago
2008
Springer
This paper investigates how the Univariate Marginal Distribution Algorithm (UMDA) behaves in non-stationary environments when engaging in sampling and selection strategies designe...