This 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.
