Section: New Results

Novelty Search

The following result has been obtained by Enrique Naredo, Leonardo Trujillo and Pierrick Legrand (CQFD member).

Novelty Search (NS) is a unique approach towards search and optimization, where an explicit objective function is replaced by a measure of solution novelty. However, NS has been mostly used in evolutionary robotics while its usefulness in classic machine learning problems has been unexplored. This work presents a NS-based Genetic Programming (GP) algorithm for supervised classification. Results show that NS can solve real-world classification tasks, validated on real-world benchmarks for binary and multiclass problems. These results are made possible by using a domain-specific behavior descriptor. Two new versions of the NS algorithm are proposed, Probabilistic NS (PNS) and a variant of Minimum Criterion NS (MCNS). The former models the behavior of each solution as a random vector and eliminates all of the original NS parameters while reducing the computational overhead of the NS algorithm. The latter uses a standard objective function to constrain and bias the search towards high performance solutions. The paper also discusses the effects of NS on GP search dynamics and code growth. Results show that NS can be used as a realistic alternative for supervised classi?cation, and for binary problems the NS algorithm exhibits an implicit bloat control ability.

Keywords: Novelty Search, Behavior-based Search, Supervised Classification, Bloat