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
Tabu Search
Autonomous Robots
Evolving Neural Control Systems
IEEE Expert: Intelligent Systems and Their Applications
Evolving neural networks through augmenting topologies
Evolutionary Computation
Making Evolution an Offer It Can't Refuse: Morphology and the Extradimensional Bypass
ECAL '01 Proceedings of the 6th European Conference on Advances in Artificial Life
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
Levels of dynamics and adaptive behavior in evolutionary neural controllers
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Advances in evolutionary computing
Information Characteristics and the Structure of Landscapes
Evolutionary Computation
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
Comparing evolutionary and temporal difference methods in a reinforcement learning domain
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolutionary Function Approximation for Reinforcement Learning
The Journal of Machine Learning Research
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Evolvability of Neuromodulated Learning for Robots
LAB-RS '08 Proceedings of the 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems
A comparison between cellular encoding and direct encoding for genetic neural networks
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Solving non-Markovian control tasks with neuroevolution
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
New usage of Sammon's mapping for genetic visualization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Exploring the T-Maze: evolving learning-like robot behaviors using CTRNNs
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
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
Evolution of central pattern generators for bipedal walking in areal-time physics environment
IEEE Transactions on Evolutionary Computation
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
IEEE Transactions on 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
Why and how to measure exploration in behavioral space
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Critical factors in the performance of novelty search
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Evolution of cartesian genetic programs for development of learning neural architecture
Evolutionary Computation
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
Effective diversity maintenance in deceptive domains
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Toward nonlinear local reinforcement learning rules through neuroevolution
Neural Computation
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Biological brains can adapt and learn from past experience. Yet neuroevolution, that is, automatically creating artificial neural networks (ANNs) through evolutionary algorithms, has sometimes focused on static ANNs that cannot change their weights during their lifetime. A profound problem with evolving adaptive systems is that learning to learn is highly deceptive. Because it is easier at first to improve fitness without evolving the ability to learn, evolution is likely to exploit domain-dependent static (i.e., nonadaptive) heuristics. This article analyzes this inherent deceptiveness in a variety of different dynamic, reward-based learning tasks, and proposes a way to escape the deceptive trap of static policies based on the novelty search algorithm. The main idea in novelty search is to abandon objective-based fitness and instead simply search only for novel behavior, which avoids deception entirely. A series of experiments and an in-depth analysis show how behaviors that could potentially serve as a stepping stone to finding adaptive solutions are discovered by novelty search yet are missed by fitness-based search. The conclusion is that novelty search has the potential to foster the emergence of adaptive behavior in reward-based learning tasks, thereby opening a new direction for research in evolving plastic ANNs.