Optimizing neural networks using faster, more accurate genetic search
Proceedings of the third international conference on Genetic algorithms
Relevance of dynamic clustering to biological networks
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
Cell division, differentiation and dynamic clustering
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
Designing Neural Networks using Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Towards the Genetic Synthesisof Neural Networks
Proceedings of the 3rd International Conference on Genetic Algorithms
Chaos as a source of complexity and diversity in evolution
Artificial Life
An artificial development model for cell pattern generation
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
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This article reports on a simple neurogenesis model that is combined with evolutionary computation. Because the integration of an evolutionary process with neural networks is such an exciting field of study, with the promise of discovering new computational models and, possibly, providing novel biological insights, much research has been conducted in this area. However, only a few studies have incorporated a development stage, and none have modeled metabolism and other chemical reactions in a consistent manner. In this article, we present a simple model of neurogenesis and cell differentiation that combines evolutionary computing, metabolism, development, and neural networks. The model represents an evolutionary large-scale chaos as a mathematical foundation. An evolutionary large-scale chaos is a large-scale chaos whose map functions change through evolutionary computing. Experiments indicate that the model is capable of evolving and growing large neural networks, and exhibits phenomena analogous to cell differentiation.