Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Advances in genetic programming
ALIFE Proceedings of the sixth international conference on Artificial life
Games of Life: Explorations in Ecology, Evolution and Behaviour
Games of Life: Explorations in Ecology, Evolution and Behaviour
Machine Learning
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Code growth in genetic programming
Code growth in genetic programming
A GP neutral function for the artificial ANT problem
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Solving the artificial ant on the Santa Fe trail problem in 20,696 fitness evaluations
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolution and complexity: The double-edged sword
Artificial Life
How novelty search escapes the deceptive trap of learning to learn
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Novelty of behaviour as a basis for the neuro-evolution of operant reward learning
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Behavioural GP diversity for adaptive stock selection
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
An adverse interaction between crossover and restricted tree depth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Coevolution of Fitness Predictors
IEEE Transactions on Evolutionary Computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Evolving plastic neural networks with novelty search
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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
Critical factors in the performance of novelty search
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
An artificial visual cortex drives behavioral evolution in co-evolved predator and prey robots
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Searching for novel classifiers
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Generic behaviour similarity measures for evolutionary swarm robotics
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Accelerating convergence in cartesian genetic programming by using a new genetic operator
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Searching for novel clustering programs
Proceedings of the 15th annual conference on Genetic and evolutionary computation
A behavior-based analysis of modal problems
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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A significant challenge in genetic programming is premature convergence to local optima, which often prevents evolution from solving problems. This paper introduces to genetic programming a method that originated in neuroevolution (i.e. the evolution of artificial neural networks) that circumvents the problem of deceptive local optima. The main idea is to search only for behavioral novelty instead of for higher fitness values. Although such novelty search abandons following the gradient of the fitness function, if such gradients are deceptive they may actually occlude paths through the search space towards the objective. Because there are only so many ways to behave, the search for behavioral novelty is often computationally feasible and differs significantly from random search. Counterintuitively, in both a deceptive maze navigation task and the artificial ant benchmark task, genetic programming with novelty search, which ignores the objective, outperforms traditional genetic programming that directly searches for optimal behavior. Additionally, novelty search evolves smaller program trees in every variation of the test domains. Novelty search thus appears less susceptible to bloat, another significant problem in genetic programming. The conclusion is that novelty search is a viable new tool for efficiently solving some deceptive problems in genetic programming.