Cambrian intelligence: the early history of the new AI
Cambrian intelligence: the early history of the new AI
ACM Computing Surveys (CSUR)
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
A novel grammar-based genetic programming approach to clustering
Proceedings of the 2005 ACM symposium on Applied computing
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Discovering several robot behaviors through speciation
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Semantic building blocks in genetic programming
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Efficiently evolving programs through the search for novelty
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Human-competitive results produced by genetic programming
Genetic Programming and Evolvable Machines
Semantically-based crossover in genetic programming: application to real-valued symbolic regression
Genetic Programming and Evolvable Machines
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
Evolving a diversity of virtual creatures through novelty search and local competition
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Predicting problem difficulty for genetic programming applied to data classification
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Defining locality as a problem difficulty measure in genetic programming
Genetic Programming and Evolvable Machines
Encouraging behavioral diversity in evolutionary robotics: An empirical study
Evolutionary Computation
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Searching for novel classifiers
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
A behavior-based analysis of modal problems
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Hi-index | 0.00 |
Novelty search (NS) is an open-ended evolutionary algorithm that eliminates the need for an explicit objective function. Instead, NS focuses selective pressure on the search for novel solutions. NS has produced intriguing results in specialized domains, but has not been applied in most machine learning areas. The key component of NS is that each individual is described by the behavior it exhibits, and this description is used to determine how novel each individual is with respect to what the search has produced thus far. However, describing individuals in behavioral space is not trivial, and care must be taken to properly define a descriptor for a particular domain. This paper applies NS to a mainstream pattern analysis area: data clustering. To do so, a descriptor of clustering performance is proposed and tested on several problems, and compared with two control methods, Fuzzy C-means and K-means. Results show that NS can effectively be applied to data clustering in some circumstances. NS performance is quite poor on simple or easy problems, achieving basically random performance. Conversely, as the problems get harder NS performs better, and outperforming the control methods. It seems that the search space exploration induced by NS is fully exploited only when generating good solutions is more challenging.