000 03422nam a22002655i 4500
008 230714s2023 si | s |||| 0|eng d
020 _a9789819918546
_9978-981-99-1854-6
041 _aeng
082 0 4 _a006.3
_223
100 1 _aJin, Yaochu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_91307489
245 1 0 _aComputational Evolution of Neural and Morphological Development
_h[electronic resource] :
_bTowards Evolutionary Developmental Artificial Intelligence /
_cby Yaochu Jin.
250 _a1st ed. 2023.
260 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2023.
300 _aXI, 295 p. 164 illus., 131 illus. in color.
_bonline resource.
490 1 _aNatural Computing Series,
_x2627-6461
505 0 _aComputational Models of Evolution and Development -- Analysis of Gene Regulatory Networks -- Evolutionary Synthesis of Gene Regulatory Dynamics -- Evolution of Morphological Development -- Evolution of Neural Development -- Computational Brain-Body Co-Evolution -- Evolutionary Morphogenetic Self-Organization of Swarm Robots -- Towards Evolutionary Developmental Systems.
520 _aThis book provides a basic yet unified overview of theory and methodologies for evolutionary developmental systems. Based on the author's extensive research into the synergies between various approaches to artificial intelligence including evolutionary computation, artificial neural networks, and systems biology, it also examines the inherent links between biological intelligence and artificial intelligence. The book begins with an introduction to computational algorithms used to understand and simulate biological evolution and development, including evolutionary algorithms, gene regulatory network models, multi-cellular models for neural and morphological development, and computational models of neural plasticity. Chap. 2 discusses important properties of biological gene regulatory systems, including network motifs, network connectivity, robustness and evolvability. Going a step further, Chap. 3 presents methods for synthesizing regulatory motifs from scratch and creating more complex regulatory dynamics by combining basic regulatory motifs using evolutionary algorithms. Multi-cellular growth models, which can be used to simulate either neural or morphological development, are presented in Chapters 4 and 5. Chap. 6 examines the synergies and coupling between neural and morphological evolution and development. In turn, Chap. 7 provides preliminary yet promising examples of how evolutionary developmental systems can help in self-organized pattern generation, referred to as morphogenetic self-organization, highlighting the great potentials of evolutionary developmental systems. Finally, Chap. 8 rounds out the book, stressing the importance and promise of the evolutionary developmental approach to artificial intelligence. Featuring a wealth of diagrams, graphs and charts to aid in comprehension, this book offers a valuable asset for graduate students, researchers and practitioners who are interested in pursuing a different approach to artificial intelligence.
650 0 _aArtificial intelligence.
650 0 _aComputational intelligence.
650 1 4 _aArtificial Intelligence.
650 2 4 _aComputational Intelligence.
856 4 0 _uhttps://doi.org/10.1007/978-981-99-1854-6
_3Click Here
887 _aAkhil Chandra Saren
942 _cEBK
999 _c1610127
_d1610123