The use of genetic algorithms and neural networks to investigate the Baldwin effect

1999 ◽  
Author(s):  
Michael Jones ◽  
Aaron Konstam
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Baldwin Effect ◽  
The Baldwin Effect

Adding Learning to the Cellular Development of Neural Networks: Evolution and the Baldwin Effect

1993 ◽  
Vol 1 (3) ◽  
pp. 213-233 ◽  
Author(s):  
Frédéric Gruau ◽  
Darrell Whitley
Keyword(s):  
Neural Networks ◽  
Boolean Functions ◽  
Development Process ◽  
Developmental Process ◽  
Fitness Landscape ◽  
Biological Cell ◽  
Baldwin Effect ◽  
Genetic Search ◽  
Lamarckian Evolution ◽  
The Baldwin Effect

A grammar tree is used to encode a cellular developmental process that can generate whole families of Boolean neural networks for computing parity and symmetry. The development process resembles biological cell division. A genetic algorithm is used to find a grammar tree that yields both architecture and weights specifying a particular neural network for solving specific Boolean functions. The current study particularly focuses on the addition of learning to the development process and the evolution of grammar trees. Three ways of adding learning to the development process are explored. Two of these exploit the Baldwin effect by changing the fitness landscape without using Lamarckian evolution. The third strategy is Lamarckian in nature. Results for these three modes of combining learning with genetic search are compared against genetic search without learning. Our results suggest that merely using learning to change the fitness landscape can be as effective as Lamarckian strategies at improving search.

Evolving Neural Networks Using the “Baldwin Effect”

1995 ◽  
pp. 333-336 ◽  
Author(s):  
Egbert J. W. Boers ◽  
Marko V. Borst ◽  
Ida G. Sprinkhuizen-Kuyper
Keyword(s):  
Neural Networks ◽  
Baldwin Effect ◽  
The Baldwin Effect

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

Sign in / Sign up

Export Citation Format

Share Document