Quadratic Bloat in Genetic Programming

In earlier work we predicted program size would grow in the limit at a quadratic rate and up to fty generations we measured bloat O(generations1:2;1:5). On two simple benchmarks we test the prediction of bloat O(generations2:0) up to generation 600. In continuous problems the limit of quadratic growth is reached but convergence in the discrete case limits growth in size. Measurements indicate subtree crossover ceases to be disruptive with large programs (1,000,000) and the population e ectively converges (even though variety is near unity). Depending upon implementation, we predict run time O(no. generations2:0;3:0) and memory O(no. generations1:0;2:0).

• Type: Conference
• Year: 2000
• Where: GECCO
• Authors: William B. Langdon
• Keywords: Case Limits Growth, GECCO 2000, Optimization, Quadratic Rate, Subtree Crossover Ceases