Towards the genetic synthesis of neural network
Proceedings of the third international conference on Genetic algorithms
Dynamic Parameter Encoding for Genetic Algorithms
Machine Learning
From SAB90 to SAB94: four years of animat research
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Automatic definition of modular neural networks
Adaptive Behavior
An introduction to natural computation
An introduction to natural computation
Evolution of Plastic Control Networks
Autonomous Robots
Evolving Artificial Neural Networks that Develop in Time
Proceedings of the Third European Conference on Advances in Artificial Life
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Localization of function via lesion analysis
Neural Computation
High-dimensional analysis of evolutionary autonomous agents
Artificial Life
Emergence of Memory-Driven Command Neurons in Evolved Artificial Agents
Neural Computation
Neutrality: a necessity for self-adaptation
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Issues in designing a neutral genotype-phenotype mapping
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
The animat contribution to cognitive systems research
Cognitive Systems Research
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This article presents a novel method for the evolution of artificial autonomous agents with small neurocontrollers. It is based on adaptive, self-organized compact genotypic encoding (SOCE) generating the phenotypic synaptic weights of the agent's neurocontroller. SOCE implements a parallel evolutionary search for neurocontroller solutions in a dynamically varying and reduced subspace of the original synaptic space. It leads to the emergence of compact successful neurocontrollers starting from large networks. The method can serve to estimate the network size needed to perform a given task, and to delineate the relative importance of the neurons composing the agent's controller network.