Neuro-Evolution and Gene Regulatory Networks
Buch, Englisch, 245 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 559 g
ISBN: 978-981-13-0199-5
Verlag: Springer Nature Singapore
Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution.
The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Introduction.- Meta-heuristics, machine learning and deep learning methods.- Evolutionary approach to deep learning.- Machine learning approach to evolutionary computation.- Evolutionary approach to gene regulatory networks.- Conclusion.