Size fair and homologous tree crossovers
Size fair and homologous crossover genetic operators for tree based genetic programming are described and tested. Both produce considerably reduced increases in program size (i.e. less bloat) and no detrimental effect on GP performance. GP search spaces are partitioned by the ridge in the number of program v. their size and depth. While search efficiency is little effected by initial conditions, these do strongly influence which half of the search space is searched. However a ramped uniform random initialisation is described which straddles the ridge. With subtree crossover trees increase about one level per generation leading to sub-quadratic bloat in program length.
|Miscellaneous (acm F.2.m), Miscellaneous (acm F.3.m), Optimization (acm G.1.6), Combinatorics (acm G.2.1), Graph Theory (acm G.2.2), PROBABILITY AND STATISTICS (acm G.3), Automatic Programming (acm I.2.2), Learning (acm I.2.6), Problem Solving, Control Methods, and Search (acm I.2.8)|
|Software (msc 68Nxx), Analysis of algorithms and problem complexity (msc 68Q25), Graph theory (including graph drawing) (msc 68R10), Learning and adaptive systems (msc 68T05), Problem solving (heuristics, search strategies, etc.) (msc 68T20)|
|Software Engineering [SEN]|
Langdon, W.B. (1999). Size fair and homologous tree crossovers. Software Engineering [SEN]. CWI.