By Topic

Coevolving Mutualists Guide Simulated Evolution

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
4 Author(s)

We show that the mutual coevolution of cooperating traits amongst interacting populations permit the solution of a matching problem. This solution, within a highly uncorrelated fitness landscape, is difficult in the absence of coevolution or other powerful agencies. We start with the GA environment of Hinton and Nowlan (1987) who originally showed that in the absence of individual learning evolving agents are not able to solve a related problem. While a number of researchers have demonstrated that the coevolution of cooperating and (in particular) competing populations of agents can improve simulated evolution, we argue that coevol ved mutualists can help evolution find a solution it otherwise could not solve (namely, selection of some particular single bit string in an uncorrelated landscape). We posit that coevolved mutualists succeed at this problem because they are able to benefit from genetically stored solutions to sub-problems. This result suggests that perhaps natural problems such as wasp/fig tree signaling, or gene-culture coevoludon of vocal learning in songbirds or human natural language may be guided by coevolutionary mutualism