Path: utzoo!news-server.csri.toronto.edu!cs.utexas.edu!sun-barr!lll-winken!uunet!mcsun!ukc!warwick!esrmm From: esrmm@warwick.ac.uk (Denis Anthony) Newsgroups: comp.ai.neural-nets Subject: Re: GENESIS 1.2ucsd Message-ID: Date: 14 Mar 91 17:51:51 GMT References: Sender: news@warwick.ac.uk (Network news) Organization: Computing Services, Warwick University, UK Lines: 23 Nntp-Posting-Host: clover In article schultz@halley.est.3m.com (John C. Schultz) writes: >I am trying to use a C code representation of a back prop neural >network as the evaluation function for a genetic algorithm >optimization routine based on the GENESIS 1.2ucsd code. > >While the concept works quite well (after a little hacking at the >interface), some optimized solution are values less than 0.0000. >Physically the output variable being optimized is a thickness and >thickness' less than 0.00 are meaningless, at least to me. > >I doubt that I can limit the neural network values (I assume that the >GA has found some strange, untrained portion of the NN to exercise?) >However, can I limit the output values of the GA to be positive? > >I would be quite happy to get a set of test points which results in a >ouput variable near 0.00000 (but positive). > Are you using total output error (or similar) as the cost function for the GA ? You could adapt the returend error by (say) giving a high cost when the result is meaningless (output < 0).