Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Learning and evolution in neural networks
Adaptive Behavior
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Autonomous Robots
Evolving neural networks through augmenting topologies
Evolutionary Computation
Every Niching Method has its Niche: Fitness Sharing and Implicit Sharing Compared
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
The Hierarchical Fair Competition (HFC) Framework for Sustainable Evolutionary Algorithms
Evolutionary Computation
ALPS: the age-layered population structure for reducing the problem of premature convergence
Proceedings of the 8th annual conference on Genetic and evolutionary computation
IEEE Transactions on Evolutionary Computation
Revising the evolutionary computation abstraction: minimal criteria novelty search
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Guarding against premature convergence while accelerating evolutionary search
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Sustaining behavioral diversity in NEAT
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
Symbolic regression using nearest neighbor indexing
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Indirectly encoding neural plasticity as a pattern of local rules
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
Towards directed open-ended search by a novelty guided evolution strategy
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
Critical factors in the performance of novelty search
Proceedings of the 13th annual conference on Genetic and evolutionary computation
On the relationships between synaptic plasticity and generative systems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Picbreeder: A case study in collaborative evolutionary exploration of design space
Evolutionary Computation
Encouraging behavioral diversity in evolutionary robotics: An empirical study
Evolutionary Computation
Novelty-Based fitness: an evaluation under the santa fe trail
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Introducing novelty search in evolutionary swarm robotics
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
Single-unit pattern generators for quadruped locomotion
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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A major goal for researchers in neuroevolution is to evolve artificial neural networks (ANNs) that can learn during their lifetime. Such networks can adapt to changes in their environment that evolution on its own cannot anticipate. However, a profound problem with evolving adaptive systems is that if the impact of learning on the fitness of the agent is only marginal, then evolution is likely to produce individuals that do not exhibit the desired adaptive behavior. Instead, because it is easier at first to improve fitness without evolving the ability to learn, they are likely to exploit domain-dependent static (i.e. non-adaptive) heuristics. This paper proposes a way to escape the deceptive trap of static policies based on the novelty search algorithm, which opens up a new avenue in the evolution of adaptive systems because it can exploit the behavioral difference between learning and non-learning individuals. The main idea in novelty search is to abandon objective-based fitness and instead simply search only for novel behavior, which avoids deception entirely and has shown prior promising results in other domains. This paper shows that novelty search significantly outperforms fitness-based search in a tunably deceptive T-Maze navigation domain because it fosters the emergence of adaptive behavior.