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Theory of Evolutionary Computation - Recent Developments in Discrete Optimization (Paperback, 1st ed. 2020)
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Theory of Evolutionary Computation - Recent Developments in Discrete Optimization (Paperback, 1st ed. 2020)
Series: Natural Computing Series
Expected to ship within 10 - 15 working days
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This edited book reports on recent developments in the theory of
evolutionary computation, or more generally the domain of
randomized search heuristics. It starts with two chapters on
mathematical methods that are often used in the analysis of
randomized search heuristics, followed by three chapters on how to
measure the complexity of a search heuristic: black-box complexity,
a counterpart of classical complexity theory in black-box
optimization; parameterized complexity, aimed at a more
fine-grained view of the difficulty of problems; and the
fixed-budget perspective, which answers the question of how good a
solution will be after investing a certain computational budget.
The book then describes theoretical results on three important
questions in evolutionary computation: how to profit from changing
the parameters during the run of an algorithm; how evolutionary
algorithms cope with dynamically changing or stochastic
environments; and how population diversity influences performance.
Finally, the book looks at three algorithm classes that have only
recently become the focus of theoretical work:
estimation-of-distribution algorithms; artificial immune systems;
and genetic programming. Throughout the book the contributing
authors try to develop an understanding for how these methods work,
and why they are so successful in many applications. The book will
be useful for students and researchers in theoretical computer
science and evolutionary computing.
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