Abstract Population initialisation in genetic programming is both easy, because random combinations of syntax can be generated straightforwardly,
and hard, because these random combinations of syntax do not always produce random and diverse…
Abstract Population initialisation in genetic programming is both easy, because random combinations of syntax can be generated straightforwardly,
and hard, because these random combinations of syntax do not always produce random and diverse program behaviours. In this
paper we perform analyses of behavioural diversity, the size and shape of starting populations, the effects of purely semantic
program initialisation and the importance of tree shape in the context of program initialisation. To achieve this, we create
four different algorithms, in addition to using the traditional ramped half and half technique, applied to seven genetic programming
problems. We present results to show that varying the choice and design of program initialisation can dramatically influence
the performance of genetic programming. In particular, program behaviour and evolvable tree shape can have dramatic effects
on the performance of genetic programming. The four algorithms we present have different rates of success on different problems.
- Content Type Journal Article
- Category Original Paper
- DOI 10.1007/s10710-009-9082-5
- Authors
- Lawrence Beadle, University of Kent Computing Laboratory Canterbury CT2 7NF UK
- Colin G. Johnson, University of Kent Computing Laboratory Canterbury CT2 7NF UK