Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Information-based objective functions for active data selection
Neural Computation
Tabu Search
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach
Proceedings of the 24th international conference on Machine learning
Sustaining diversity using behavioral information distance
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
How novelty search escapes the deceptive trap of learning to learn
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Revising the evolutionary computation abstraction: minimal criteria novelty search
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Efficiently evolving programs through the search for novelty
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
Genetic programming needs better benchmarks
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Introducing novelty search in evolutionary swarm robotics
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
Evolving plastic neural networks for online learning: review and future directions
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Generic behaviour similarity measures for evolutionary swarm robotics
Proceedings of the 15th annual conference on Genetic and evolutionary computation
A true finite-state baseline for tartarus
Proceedings of the 15th annual conference on Genetic and evolutionary computation
An effective parse tree representation for tartarus
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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The idea of evolving novel rather than fit solutions has recently been offered as a way to automatically discover the kind of complex solutions that exhibit truly intelligent behavior. So far, novelty search has only been studied in the context of problems where the number of possible "different" solutions has been limited. In this paper, we show, using a task with a much larger solution space, that selecting for novelty alone does not offer an advantage over fitness-based selection. In addition, we examine how the idea of novelty search can be used to sustain diversity and improve the performance of standard, fitness-based search.