Experimentation is necessary - a purely theoretical approach is not
reasonable. The new experimentalism, a development in the modern
philosophy of science, considers that an experiment can have a life
of its own. It provides a statistical methodology to learn from
experiments, where the experimenter should distinguish between
statistical significance and scientific meaning.
This book introduces the new experimentalism in evolutionary
computation, providing tools to understand algorithms and programs
and their interaction with optimization problems. The book develops
and applies statistical techniques to analyze and compare modern
search heuristics such as evolutionary algorithms and particle
swarm optimization. Treating optimization runs as experiments, the
author offers methods for solving complex real-world problems that
involve optimization via simulation, and he describes successful
applications in engineering and industrial control projects.
The book bridges the gap between theory and experiment by
providing a self-contained experimental methodology and many
examples, so it is suitable for practitioners and researchers and
also for lecturers and students. It summarizes results from the
author's consulting to industry and his experience teaching
university courses and conducting tutorials at international
conferences. The book will be supported online with downloads and
exercises.
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