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 89
... random and some portion of the strings are exchanged between the strings to create two new strings . In a single - point crossover operator , this is performed by randomly choosing a crossing site along the string and by exchanging all ...
... random and some portion of the strings are exchanged between the strings to create two new strings . In a single - point crossover operator , this is performed by randomly choosing a crossing site along the string and by exchanging all ...
Seite 190
Kalyanmoy Deb. 5.7 Random Weighted GA * Murata and Ishibuchi ( 1995 ) suggested a random weighted GA ( RWGA ) similar to the above WBGA , except that a random normalized weight vector w ( i ) ( w1 , w2 , ... , WM ) is assigned to the i ...
Kalyanmoy Deb. 5.7 Random Weighted GA * Murata and Ishibuchi ( 1995 ) suggested a random weighted GA ( RWGA ) similar to the above WBGA , except that a random normalized weight vector w ( i ) ( w1 , w2 , ... , WM ) is assigned to the i ...
Seite 339
... random solutions . Random search methods are likely to face difficulties in finding the Pareto - optimal front in the case with y close to zero , mainly due to the low density of solutions towards the Pareto - optimal region . Parameter ...
... random solutions . Random search methods are likely to face difficulties in finding the Pareto - optimal front in the case with y close to zero , mainly due to the low density of solutions towards the Pareto - optimal region . Parameter ...
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 |