Genetic programming and evolutionary generalization
IEEE Transactions on Evolutionary Computation, 6 (5):
431--442 (October 2002)Abstract
In genetic programming (GP), learning problems can be
classified broadly into two types: those using data
sets, as in supervised learning, and those using an
environment as a source of feedback. An increasing
amount of research has concentrated on the robustness
or generalisation ability of the programs evolved using
GP. While some of the researchers report on the
brittleness of the solutions evolved, others proposed
methods of promoting robustness/generalization. It is
important that these methods are not ad hoc and are
applicable to other experimental setups. In this paper,
learning concepts from traditional machine learning and
a brief review of research on generalisation in GP are
presented. The paper also identifies problems with
brittleness of solutions produced by GP and suggests a
method for promoting robustness/generalisation of the
solutions in simulating learning behaviours using GP.
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