Genetic algorithms have their roots in the 1950s, which marked the first attempt at computer-based simulation of the process of evolution. Since then genetic algorithms have been applied in a range of settings, from modeling chemical and biochemical processes, to generating solutions to problems involving scheduling and logistics.
This book describes some of the work being done on the employment of genetic algorithms in real-world situations as diverse as electric power systems, plasma chemical reactors, timetabling, optimizing cargo weight distribution, and portfolio management in computer-based trading. Certain chapters will also focus on general topics such as the use of genetic algorithms for optimization of control systems.
Many of the chapters will discuss hybrid approaches which combine genetic algorithms with other techniques like neural networks and heuristic searching. The use of hybrid techniques is especially relevant to complex processes, and can offer significant cost and time savings.
The broad scope of situations covered means that this book will interest a range of specialists in science, engineering, manufacturing and transport/logistics, as well as computer scientists and researchers at the cutting edge of this field.
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