Andere Kunden interessierten sich auch für
![Classification Problem in Data Mining Using Decision Trees]( "Classification Problem in Data Mining Using Decision Trees")
Classification Problem in Data Mining Using Decision Trees39,99 €![A Heuristic Based Solution for Rectangle Packing Problem]( "A Heuristic Based Solution for Rectangle Packing Problem")
A Heuristic Based Solution for Rectangle Packing Problem56,99 €![How to cope with an Intractable Problem]( "How to cope with an Intractable Problem")
How to cope with an Intractable Problem29,99 €![Colourful Linear Programming Feasibility Problem]( "Colourful Linear Programming Feasibility Problem")
Colourful Linear Programming Feasibility Problem35,99 €![Parallel Genetic Algorithmic approach for solving NRP]( "Parallel Genetic Algorithmic approach for solving NRP")
Parallel Genetic Algorithmic approach for solving NRP26,99 €![The Multi-Criteria Ranking Approach to Classification Algorithms]( "The Multi-Criteria Ranking Approach to Classification Algorithms")
The Multi-Criteria Ranking Approach to Classification Algorithms29,99 €![Database Hiding On Tag Web Using Steganography by Genetic Algorithm]( "Database Hiding On Tag Web Using Steganography by Genetic Algorithm")
Database Hiding On Tag Web Using Steganography by Genetic Algorithm34,99 €
In this book, we consider the benchmark quadratic assignment problem which is very difficult NP-hard problem that has several practical applications. Several exact and heuristic algorithms are developed for solving the problem. In general, large sized instances cannot easily be solved optimally by an exact algorithm, but there are some situations where only exact optimal solution is required. Hence, we first present a reformulation of the problem, and then we apply simple and data-guided lexisearch algorithm to obtain exact optimal solutions to the problem. We also develop simple and improved genetic algorithms using sequential constructive crossover operator to find heuristic solution to the problem. Finally, a hybrid algorithm that combines lexisearch and genetic algorithms is developed. The proposed algorithm uses lexisearch algorithm to generate initial population, self-adaptive three crossover operators, and randomly one of four mutation operators, restricted combined mutation operator as local search, and multi-parent sequential constructive crossover as immigration method. Experimental results on benchmark QAPLIB instances show the effectiveness of the developed algorithms.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.