LEADER 04161nam 22005175 450 001 996546827303316 005 20230719191257.0 010 $a981-9918-54-5 024 7 $a10.1007/978-981-99-1854-6 035 $a(MiAaPQ)EBC30647823 035 $a(Au-PeEL)EBL30647823 035 $a(DE-He213)978-981-99-1854-6 035 $a(PPN)272250740 035 $a(EXLCZ)9927594372800041 100 $a20230714d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputational Evolution of Neural and Morphological Development$b[electronic resource] $eTowards Evolutionary Developmental Artificial Intelligence /$fby Yaochu Jin 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (302 pages) 225 1 $aNatural Computing Series,$x2627-6461 311 08$aPrint version: Jin, Yaochu Computational Evolution of Neural and Morphological Development Singapore : Springer,c2023 9789819918539 327 $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. 330 $aThis book provides a basic yet unified overview of theory and methodologies for evolutionary developmental systems. Based on the author?s extensive research into the synergies between various approaches to artificial intelligence including evolutionary computation, artificial neural networks, and systems biology, it also examines the inherent links between biological intelligence and artificial intelligence. The book begins with an introduction to computational algorithms used to understand and simulate biological evolution and development, including evolutionary algorithms, gene regulatory network models, multi-cellular models for neural and morphological development, and computational models of neural plasticity. Chap. 2 discusses important properties of biological gene regulatory systems, including network motifs, network connectivity, robustness and evolvability. Going a step further, Chap. 3 presents methods for synthesizing regulatory motifs from scratch and creating more complex regulatory dynamics by combining basic regulatory motifs using evolutionary algorithms. Multi-cellular growth models, which can be used to simulate either neural or morphological development, are presented in Chapters 4 and 5. Chap. 6 examines the synergies and coupling between neural and morphological evolution and development. In turn, Chap. 7 provides preliminary yet promising examples of how evolutionary developmental systems can help in self-organized pattern generation, referred to as morphogenetic self-organization, highlighting the great potentials of evolutionary developmental systems. Finally, Chap. 8 rounds out the book, stressing the importance and promise of the evolutionary developmental approach to artificial intelligence. Featuring a wealth of diagrams, graphs and charts to aid in comprehension, this book offers a valuable asset for graduate students, researchers and practitioners who are interested in pursuing a different approach to artificial intelligence. 410 0$aNatural Computing Series,$x2627-6461 606 $aArtificial intelligence 606 $aComputational intelligence 606 $aArtificial Intelligence 606 $aComputational Intelligence 615 0$aArtificial intelligence. 615 0$aComputational intelligence. 615 14$aArtificial Intelligence. 615 24$aComputational Intelligence. 676 $a006.3823 700 $aJin$b Yaochu$0977855 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996546827303316 996 $aComputational Evolution of Neural and Morphological Development$93403596 997 $aUNISA