Genetic Algorithms (GAs) are
widely applied to underlie complex optimization
techniques. Genetic Algorithms is one of the most
successful and widely used Evolutionary Algorithm
approaches that pertain to several other
evolutionary inspired heuristic approaches; Genetic
Programming (GP), Evolutionary Programming (EP) and
Evolutionary Strategies (ESs). GAs are search and
optimization algorithms inspired by biological
evolutionary processes. They have been highly
successful as techniques for getting computers to
automatically solve problems relying on evolutionary
heuristics. Since their inception more than thirty
years ago GAs have been used to solve complex
computational problems but along with this
engineering aspect there has been a growing interest
in their theoretical bases and various
implementations.
This objective of this book is to
present the latest state-of-the-art theories,
methodologies and applications of GAs. It deals
with the theoretical and methodological aspects, as
well as various applications in a variety of
disciplines. GAs are applied to real world problems
from science, technology, biology, chemistry, social
sciences, business and commerce. This book comprises
of several chapters including theories and
applications giving the fundamentals and many
important research challenges in this field of
study.
This book has been designed to be
beneficial to most practicing scientists and
engineers interested in this area.
Herewith we invite all qualified authors interested
in GAs to join our team to add a landmark to this
research area.
GAs constitutes one of the most
successful and widely used Evolutionary Algorithms.
Other commonly used and similarly successful EA
techniques are Genetic Programming (GP),
Evolutionary Programming (EP) and Evolution
Strategies (ES). Evolutionary Algorithms
