Multi-Objective Optimization Using Evolutionary AlgorithmsWiley, 05.07.2001 - 497 Seiten Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run.
The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study. |
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Seite 33
... dominated set of solutions P ' are those that are not strongly dominated by any other member of the set P. The above definition suggests that a weakly non - dominated set found from a set of P solutions contains all members of the non ...
... dominated set of solutions P ' are those that are not strongly dominated by any other member of the set P. The above definition suggests that a weakly non - dominated set found from a set of P solutions contains all members of the non ...
Seite 34
... dominated set , the dominance relation ( or < , as the case may be ) can be used to identify the better of two given solutions . Like the existence of different algorithms for finding the minimum number from a finite set ( Cormen et al ...
... dominated set , the dominance relation ( or < , as the case may be ) can be used to identify the better of two given solutions . Like the existence of different algorithms for finding the minimum number from a finite set ( Cormen et al ...
Seite 40
... dominated front in a population . These algorithms classify the population into two sets the non - dominated set and the remaining dominated set . However , there exist some algorithms which require the entire population to be ...
... dominated front in a population . These algorithms classify the population into two sets the non - dominated set and the remaining dominated set . However , there exist some algorithms which require the entire population to be ...
Inhalt
Prologue | 1 |
ParetoOptimality | 32 |
Classical Methods | 48 |
Urheberrecht | |
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Andere Ausgaben - Alle anzeigen
Multi-Objective Optimization using Evolutionary Algorithms Kalyanmoy Deb Eingeschränkte Leseprobe - 2001 |
MULTI-OBJECTIVE OPTIMIZATION USING EVOLUTIONARY ALGORITHMS Kalyanmoy Deb Keine Leseprobe verfügbar - 2010 |
Multi-Objective Optimization Using Evolutionary Algorithms Kalyanmoy Deb Keine Leseprobe verfügbar - 2009 |
Häufige Begriffe und Wortgruppen
best non-dominated better calculated choose chosen constraint violation convergence convex corresponding created crossover operator decision variable space discussed distance distribution diversity dominated solutions elite elitist equation Euclidean distance evaluated evolution strategy evolutionary algorithms Evolutionary Computation f₁ feasible solution fitness values genetic algorithm genetic operations goal programming goal programming problem hypercube infeasible solutions local search mating pool maximum metric Minimize minimum MOEA multi-objective optimization problem mutation operator niche count non-dominated front non-dominated set nonconvex NPGA NSGA NSGA-II number of solutions objective function values objective space obtained non-dominated solutions obtained solutions offspring population optimal solutions optimum Oshare parameter parent solutions Pareto Pareto-optimal region Pareto-optimal set Pareto-optimal solutions performed population members procedure random real-parameter schema search space selection operator set of solutions shown in Figure shows simulation solving SPEA Step strategy string subpopulation suggested technique test problems tournament selection trade-off solutions true Pareto-optimal front w₁ WBGA weight vector
Verweise auf dieses Buch
Data Mining and Knowledge Discovery with Evolutionary Algorithms Alex A. Freitas Eingeschränkte Leseprobe - 2002 |