Genetic programming: Difference between revisions

Revision as of 05:29, 23 December 2003

Revision as of 17:31, 16 February 2004

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So far GPs have successfully solved some toy problems, such as the lawn mower problem, but the method is very computationally intensive, and may not compare favourably where simpler methods, such as [[genetic algorithm]]s or [[random optimisation]] can be used instead. It is possible that some more complex problems may be more amenable to solution using GPs than other optimization methods.

Unfortunately, due to the lack of solid theory regarding the performance of genetic programming vs. traditional search methods (such as [[hill- climbing]]), genetic programming remains a sort of pariah amongst the various techniques of search. While genetic programming has achieved results that are as good as and sometimes better than human-generated results, more work needs to be done on the theory in order to bring the technique into more widespread use.

== Bibliography ==