Evolutionary algorithms (EAs) are becoming increasingly
attractive for researchers from various disciplines, such as
operations research, computer science, industrial engineering,
electrical engineering, social science, economics, etc. This book
presents an insightful, comprehensive, and up-to-date treatment of
EAs, such as genetic algorithms, differential evolution, evolution
strategy, constraint optimization, multimodal optimization,
multiobjective optimization, combinatorial optimization, evolvable
hardware, estimation of distribution algorithms, ant colony
optimization, particle swarm optimization, artificial immune
systems, artificial life, genetic programming, etc.
It emphasises the initiative ideas of the algorithm, contains
discussions in the contexts, and suggests further readings and
possible research projects. All the methods form a pedagogical way
to make EAs easy and interesting.
This textbook also introduces the applications of EAs as many as
possible. At least one real-life application is introduced by the
end of almost every chapter. The authors focus on the kernel part
of applications, such as how to model real-life problems, how to
encode and decode the individuals, how to design effective search
operators according to the chromosome structures, etc.
This textbook adopts pedagogical ways of making EAs easy and
interesting. Its methods include an introduction at the beginning
of each chapter, emphasising the initiative, discussions in the
contexts, summaries at the end of every chapter, suggested further
reading, exercises, and possible research projects.
Introduction to Evolutionary Algorithms will enable students
to:
establish a strong background on evolutionary algorithms;
appreciate the cutting edge of EAs;
perform their own research projects by simulating the
application introduced in the book; and
apply their intuitive ideas to academic search.
This book is aimed at senior undergraduate students or
first-year graduate students as a textbook or self-study
material."
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