Fitness causes bloat: Mutation
Author(s) -
William B. Langdon,
Riccardo Poli
Publication year - 1998
Publication title -
lecture notes in computer science
Language(s) - English
Resource type - Book series
SCImago Journal Rank - 0.249
H-Index - 400
eISSN - 1611-3349
pISSN - 0302-9743
ISBN - 3-540-64360-5
DOI - 10.1007/bfb0055926
Subject(s) - fitness function , redundancy (engineering) , computer science , genetic programming , variable (mathematics) , representation (politics) , mutation , selection (genetic algorithm) , fitness landscape , genetic algorithm , mathematical optimization , fitness approximation , feature selection , artificial intelligence , mathematics , machine learning , biology , genetics , mathematical analysis , population , demography , sociology , politics , political science , law , gene , operating system
. The problem of evolving, using mutation, an artificial ant to follow the Santa Fe trail is used to study the well known genetic program-ming feature of growth in solution length. Known variously as "bloat", "flu " and increasing "structural complexity", this is often described in terms of increasing "redundancy" in the code caused by "introns". Comparison between runs with and without fitness selection pressure, backed by Price's Theorem, shows the tendency for solutions to grow in size is caused by fitness based selection. We argue that such growth is inherent in using a fixed evaluation function with a discrete but variable length representation. With simple static evaluation search converges to mainly finding trial solutions with the same fitness as existing trial solu-tions. In general variable length allows many more long representations of a given solution than short ones. Thus in search (without a length bias) we expect longer representations to occur more often and so repre-sentation length to tend to increase. I. e. fitness based selection leads to bloat.
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