Metaheuristic algorithms can be considered the epitome of unsupervised learning algorithms for optimization of engineering and artificial intelligence problems, including simulated annealing (SA), tabu search (TS), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), differential evolution (DE), and others. Distinct from most supervised learning algorithms that need labeled data to learn and construct determination models, metaheuristic algorithms inherit characteristics... Celý popis
Koupit za 3 720 Kč- Od nejoblíbenějších
- Od nejlevnějších
- Od nejdražších
Popis
Metaheuristic algorithms can be considered the epitome of unsupervised learning algorithms for optimization of engineering and artificial intelligence problems, including simulated annealing (SA), tabu search (TS), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), differential evolution (DE), and others. Distinct from most supervised learning algorithms that need labeled data to learn and construct determination models, metaheuristic algorithms inherit characteristics of unsupervised learning algorithms used for solving complex engineering optimization problems without labeled data, just like self-learning, to find solutions to complex problems.
Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications provides a brief introduction to metaheuristic algorithms from the ground up; from the basic ideas
Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications provides a brief introduction to metaheuristic algorithms from the ground up; from the basic ideas
Parametry
| Rok vydání | 2023 |
| Počet stran | 622 |
| Výrobce | Elsevier Science Publishing Co Inc |
| Jazyk | anglické |
| Váha | 450 gramů |
