Name
Title | ||
|---|---|---|
Evolution and learning in neural networks: dynamic correlation, relearning and thresholding |
Abstract | ||
|---|---|---|
This contribution revisits an earlier discovered observation that the average performance of a population of neural networks that are evolved to solve one task is improved by lifetime learning on a different task. Two extant, and very different, explanations of this phenomenon are examined dynamic correlation, and relearning. Experimental results are presented which suggest that neither of these hypotheses can fully explain the phenomenon. A new explanation of the effect is proposed and empirically justified. This explanation is based on the fact that in these, and many other related studies, real-valued neural network outputs are thresholded to provide discrete actions. The effect of such thresholding produces a particular type of fitness landscape in which lifetime learning can reduce the deleterious effects of mutation, and therefore increase mean population fitness. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1177/105971230000800305 | Adaptive Behaviour |
Keywords | Field | DocType |
dynamic correlation,neural network,fitness landscape,genetic algorithm,neural networks,machine learning | Competitive learning,Population,Fitness landscape,Computer science,Wake-sleep algorithm,Recurrent neural network,Types of artificial neural networks,Time delay neural network,Artificial intelligence,Deep learning,Machine learning | Journal |
Volume | Issue | ISSN |
8 | 3-4 | 1059-7123 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brian Carse | 1 | 259 | 26.31 |
| Johan Oreland | 2 | 0 | 0.68 |