Multiobjective Evolutionary Algorithm

From GM-RKB
Jump to navigation Jump to search

A Multiobjective Evolutionary Algorithm is an Evolutionary Algorithm that can solve a Multiobjective Optimization Task.

References

2007

  • (Coello Coello et al., 2007) ⇒ Carlos A. Coello Coello, Gary B. Lamont, and David A. Van Veldhuizen. (2007). “Evolutionary Algorithms for Solving Multi-Objective Problems." Springer. ISBN:0387332545
    • Synopsis: Solving multi-objective problems is an evolving effort, and computer science and other related disciplines have given rise to many powerful deterministic and stochastic techniques for addressing these large-dimensional optimization problems. Evolutionary algorithms are one such generic stochastic approach that has proven to be successful and widely applicable in solving both single-objective and multi-objective problems. This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems, including test suites with associated performance based on a variety of appropriate metrics, as well as serial and parallel algorithm implementations.
    • Google Key words and phrases: multiobjective optimization, Genetic Algorithms, Evolutionary Algorithms, Pareto optimal, Evolutionary Computation, Tabu search, simulated annealing, Pareto dominance, evolution strategy, MOEA, Differential Evolution, Particle Swarm Optimization, objective function, local search, fitness function, Metaheuristics, memetic, IEEE, Computer Science, pMOEA

1999

Retrieved from "http://www.gabormelli.com/RKB/index.php?title=Multiobjective_Evolutionary_Algorithm&oldid=508363"