<P>Evolutionary Computation for Optimization and Modeling is an introduction to evolutionary computation, a field which includes genetic algorithms, evolutionary programming, evolution strategies, and genetic programming. The text is a survey of some application of evolutionary algorithms. It introd
Evolutionary Computation for Modeling and Optimization
✍ Scribed by Daniel Ashlock
- Publisher
- Springer
- Year
- 2006
- Tongue
- English
- Leaves
- 578
- Series
- Interdisciplinary Applied Mathematics
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
✦ Synopsis
Evolutionary computation includes Genetic Algorithms, Evolutionary Programming, Evolution Strategies, and Genetic Programming. In general any population based, selectionist algorithm that performs optimization or supports modeling is a form of evolutionary computation. This text covers primarily genetic algorithms and genetic programming as well as variations based on student projects and the author's research. It substantially reflects engineering (problem solving) rather than mathematical (theorem proving) methods. This book should appeal to undergraduates and beginning graduates inmathematics, computer science, engineering and biology.
✦ Subjects
Информатика и вычислительная техника;Искусственный интеллект;Эволюционные алгоритмы;
📜 SIMILAR VOLUMES
<p><P>Evolutionary Computation for Optimization and Modeling is an introduction to evolutionary computation, a field which includes genetic algorithms, evolutionary programming, evolution strategies, and genetic programming. The text is a survey of some application of evolutionary algorithms. It int
Concentrates on developing intuition about evolutionary computation and problem solving skills and tool sets. Lots of applications and test problems, including a biotechnology chapter.
Budget Optimization and Allocation: An Evolutionary Computing Based Model is a guide for computer programmers for writing algorithms for efficient and effective budgeting. It provides a balance of theory and practice. Chapters explain evolutionary computational techniques (genetic algorithms) and co
<p><p>This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems. The motivation for this book arises from the fact that many real-world optimization problems and engineering systems are subject to dynamic environments,