Evolution and Learning in Neural Networks: Dynamic Correlation, Relearning and Thresholding

This contribution revisits an earlier discovered observation that the average performance of a pop ulation 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 relat ed 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 learn ing can reduce the deleterious effects of mutation, and therefore increase mean population fitness. © 2000, Sage Publications. All rights reserved

How Symbiosis Can Guide Evolution

Hinton and Nowlan have demonstrated a model of how lifetime plasticity can guide evolution. They show how acquired traits change the shape of the reward landscape in which subsequent genetic variation takes place, and in so doing encourage the discovery of equivalent heritable traits. This enables the seemingly Lamarkian inheritance of acquired characteristics without the direct transfer of information from the phenotype to the genotype. This paper draws direct inspiration from their work to illustrate a different phenomenon. We demonstrate how the formation of symbiotic relationships in an ecosystem can guide the course of subsequent genetic variation. This phenomenon can be described as two phases: First, symbiotic groups find solutions where individual organisms cannot, simply because lifetime interaction produces new combinations of abilities more rapidly than the relatively slow genetic variation of individuals. Second, these symbiotic groups subsequently change the shape of the reward landscape for evolution, providing a gradient that guides genetic variation to the same solution. Ultimately, an individual organism exhibits the capabilities formerly exhibited by the group. This process enables the combination of characteristics from organisms of distinct species without direct transfer of genetic information