This book provides theoretical and practical knowledge about a methodology for evolutionary
algorithm-based search strategy with the integration of several machine learning and deep
learning techniques. These include convolutional neural networks Gröbner bases relevance
vector machines transfer learning bagging and boosting methods clustering techniques
(affinity propagation) and belief networks among others. The development of such tools
contributes to better optimizing methodologies. Beginning with the essentials of evolutionary
algorithms and covering interdisciplinary research topics the contents of this book are
valuable for different classes of readers: novice intermediate and also expert readers from
related fields. 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.
