Introduction to parallel computing: design and analysis of algorithms
Introduction to parallel computing: design and analysis of algorithms
Task scheduling in parallel and distributed systems
Task scheduling in parallel and distributed systems
Distributed programming with Java
Distributed programming with Java
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
A Short Tutorial on Evolutionary Multiobjective Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Analysis of linkage-friendly genetic algorithms
Analysis of linkage-friendly genetic algorithms
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Parallel genetic algorithm for SPICE model parameter extraction
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
ISPA'06 Proceedings of the 2006 international conference on Frontiers of High Performance Computing and Networking
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The concepts of efficiency and effectiveness must be addressed in conducting research into using a Evolutionary Algorithm (EA) for optimization problems. The increased use of evolutionary approaches for real-world applications, containing multiple objectives and high dimensionality, has led to the design and generation of a number of Multiobjective Evolutionary Algorithms (MOEA). When analyzing these algorithms, the issues of effectiveness and efficiency are extremely important and typically drive the urge to parallelize these algorithms. The parallelization of MOEAs is a relatively new concept, with few researchers contributing work in this area. This parallelization process is not a simple task and involves the analysis of various parallel models and the parameters associated with these models. This paper presents a thorough analysis of the various parallel MOEA models, the issues associated with these models and recommendations for using these models in MOEAs. In particular, these parallelization concepts are applied to the Multiobjective Messy Genetic Algorithm II.