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 135
... variable vector ( Schwefel , 1987a ) . Beyer ( 1995b ) has shown that , for the sphere model , the optimal learning ... decision variable vector . The logarithmic update rules for decision variables and mutation strengths are as follows ...
... variable vector ( Schwefel , 1987a ) . Beyer ( 1995b ) has shown that , for the sphere model , the optimal learning ... decision variable vector . The logarithmic update rules for decision variables and mutation strengths are as follows ...
Seite 328
... decision variable values corresponding to Pareto - optimal solution O and Pareto - optimal regions A , B and C. The Pareto - optimal solutions for the overall three - dimensional decision variable space and for the three individual pair ...
... decision variable values corresponding to Pareto - optimal solution O and Pareto - optimal regions A , B and C. The Pareto - optimal solutions for the overall three - dimensional decision variable space and for the three individual pair ...
Seite 360
... decision variable space will resemble that of the objective space . However , for a nonlinear g ( x ) function , the decision variable space can be very different . Since evolutionary search operators work on the decision variable space ...
... decision variable space will resemble that of the objective space . However , for a nonlinear g ( x ) function , the decision variable space can be very different . Since evolutionary search operators work on the decision variable space ...
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 |