An Indirect Block-Oriented Representation for Genetic Programming
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
An adverse interaction between crossover and restricted tree depth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Reducing population size while maintaining diversity
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
| Hi-index | 0.00 |
Hierarchical genetic programming (HGP) approaches rely on the discovery, modification, and use of new functions to accelerate evolution. This paper provides a qualitative explanation of the improved behavior of HGP, based on an analysis of the evolution process from the dual perspective of diversity and causality. From a static point of view, the use of an HGP approach enables the manipulation of a population of higher diversity programs. Higher diversity increases the exploratory ability of the genetic search process, as demonstrated by theoretical and experimental fitness distributions and expanded structural complexity of individuals. From a dynamic point of view, this report analyzes the causality of the crossover operator. Causality relates changes in the structure of an object with the effect of such changes, i.e., changes in the properties or behavior of the object. The analyses of crossover causality suggests that HGP discovers and exploits useful structures in a bottom-up, hierarchical manner. Diversity and causality are complementary, affecting exploration and exploitation in genetic search. Unlike other machine learning techniques that need extra machinery to control the tradeoff between them, HGP automatically trades off exploration and exploitation.