Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Incremental evolution of complex general behavior
Adaptive Behavior - Special issue on environment structure and behavior
ALIFE Proceedings of the sixth international conference on Artificial life
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Evolving Neural Control Systems
IEEE Expert: Intelligent Systems and Their Applications
Evolving neural networks through augmenting topologies
Evolutionary Computation
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Evolutionary Computation
Evolutionary Computation
Evolutionary Function Approximation for Reinforcement Learning
The Journal of Machine Learning Research
Compositional pattern producing networks: A novel abstraction of development
Genetic Programming and Evolvable Machines
Picbreeder: evolving pictures collaboratively online
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
Picbreeder: A case study in collaborative evolutionary exploration of design space
Evolutionary Computation
Efficient non-linear control through neuroevolution
ECML'06 Proceedings of the 17th European conference on Machine Learning
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Critical factors in the performance of hyperNEAT
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Evolutionary algorithms are often evaluated by measuring and comparing their ability to consistently reach objectives chosen a priori by researchers. Yet recent results from experiments without explicit a priori objectives, such as in Picbreeder and with the novelty search algorithm, raise the question of whether the very act of setting an objective is exacting a subtle price. Nature provides another hint that the reigning objective-based paradigm may be obfuscating evolutionary computation's true potential; after all, many of the greatest discoveries of natural evolution, such as flight and human-level intelligence, were not set as a priori objectives at the beginning of the search. The dangerous question is whether such triumphs only result because they were not objectives. To examine this question, this paper takes the unusual experimental approach of attempting to re-evolve images that were already once evolved on Picbreeder. In effect, images that were originally discovered serendipitously become a priori objectives for a new experiment with the same algorithm. Therefore, the resulting failure to reproduce the very same results cannot be blamed on the evolutionary algorithm, setting the stage for a contemplation of the price we pay for evaluating our algorithms only for their ability to achieve preconceived objectives.