Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Artificial intelligence (3rd ed.)
Artificial intelligence (3rd ed.)
What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Machine Learning - Special issue on genetic algorithms
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
The dynamics of recurrent behavior networks
Adaptive Behavior
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Bayesian optimization algorithm: from single level to hierarchy
Bayesian optimization algorithm: from single level to hierarchy
Compositional Evolution: The Impact of Sex, Symbiosis, and Modularity on the Gradualist Framework of Evolution (Vienna Series in Theoretical Biology)
Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions
Evolutionary Computation
Artificial Life
Modular Interdependency in Complex Dynamical Systems
Artificial Life
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
Overcoming hierarchical difficulty by hill-climbing the building block structure
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A building-block royal road where crossover is provably essential
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Nonlinear optimization using generalized hopfield networks
Neural Computation
Binary Optimization: On the Probability of a Local Minimum Detection in Random Search
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Adaptive Networks: Theory, Models and Applications
Adaptive Networks: Theory, Models and Applications
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Individual selection for cooperative group formation
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
The neuronal replicator hypothesis
Neural Computation
Can selfish symbioses effect higher-level selection?
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
Symbiosis enables the evolution of rare complexes in structured environments
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
Genetic assimilation and canalisation in the baldwin effect
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
The Knowledge Engineering Review
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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The natural energy minimization behavior of a dynamical system can be interpreted as a simple optimization process, finding a locally optimal resolution of problem constraints. In human problem solving, high-dimensional problems are often made much easier by inferring a low-dimensional model of the system in which search is more effective. But this is an approach that seems to require top-down domain knowledgeâ聙聰not one amenable to the spontaneous energy minimization behavior of a natural dynamical system. However, in this article we investigate the ability of distributed dynamical systems to improve their constraint resolution ability over time by self-organization. We use a â聙聵â聙聵self-modelingâ聙聶â聙聶 Hopfield network with a novel type of associative connection to illustrate how slowly changing relationships between system components can result in a transformation into a new system which is a low-dimensional caricature of the original system. The energy minimization behavior of this new system is significantly more effective at globally resolving the original system constraints. This model uses only very simple, and fully distributed, positive feedback mechanisms that are relevant to other â聙聵â聙聵active linkingâ聙聶â聙聶 and adaptive networks. We discuss how this neural network model helps us to understand transformations and emergent collective behavior in various non-neural adaptive networks such as social, genetic and ecological networks.