Genetic algorithms + data structures = evolution programs - Info and Reading Options
By Zbigniew Michalewicz
“Genetic algorithms + data structures = evolution programs” Metadata:
- Title: ➤ Genetic algorithms + data structures = evolution programs
- Author: Zbigniew Michalewicz
“Genetic algorithms + data structures = evolution programs” Subjects and Themes:
- Subjects: ➤ Computer algorithms - Computer programs - Data structures (Computer science) - Evolutionary programming (Computer science) - Genetic algorithms - Logiciels - Optimisation numérique - Saisie des données (Informatique) - Problème transport - Algorithmes - Algorithme génétique - Partitionnement - Genetischer Algorithmus - Computadoras digitales (Programación) - Structures de données (Informatique) - Algorithmique - Estructura de datos (Ciencia de la computación) - Ordonnancement - Voyageur commerce - Structure donnée - Programowanie ewolucyjne - Algorytmy genetyczne - Optymalizacja - Struktury danych (informatyka) - Numerical analysis - Algorithms - Artificial intelligence - Software engineering - Operations research - Computer science - Qa76.9.a43 m53 1994 - 005.1
Edition Identifiers:
- The Open Library ID: OL47602W
AI-generated Review of “Genetic algorithms + data structures = evolution programs”:
"Genetic algorithms + data structures = evolution programs" Description:
The Open Library:
Genetic algorithms are founded upon the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control. The importance of these techniques has been growing in the last decade, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science. The book is self-contained and the only prerequisite is basic undergraduate mathematics. It is aimed at researchers, practitioners, and graduate students in computer science and artificial intelligence, operations research, and engineering. This second edition includes several new sections and many references to recent developments. A simple example of genetic code and an index are also added. Writing an evolution program for a given problem should be an enjoyable experience - this book may serve as a guide to this task.
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