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Benchmark Functions for the CEC'2008 Special Session and Competition on Large Scale Global Optimization
Technology, the University of Science and Technology of China, Hefei, Anhui, China The Center of Excellence for Research in Computational Intelligence and Applications (CERCIA), School of ComputerExpand
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Applying Family Competition to Evolution Strategies for Constrained Optimization
This paper applies family competition to evolution strategies to solve constrained optimization problems. The family competition of Family Competition Evolution Strategy (FCES) can be viewed as aExpand
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PFRF: An adaptive data replication algorithm based on star-topology data grids
Recently, data replication algorithms have been widely employed in data grids to replicate frequently accessed data to appropriate sites. The purposes are shortening file transmission distance andExpand
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Enhancing MOEA/D with guided mutation and priority update for multi-objective optimization
Multi-objective optimization is an essential and challenging topic in the domains of engineering and computation because real-world problems usually include several conflicting objectives. CurrentExpand
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Genetic Algorithm Design Inspired by Organizational Theory: Pilot Study of a Dependency Structure Matrix Driven Genetic Algorithm
This study proposes a dependency structure matrix driven genetic algorithm (DSMDGA) which utilizes the dependency structure matrix (DSM) clustering to extract building block (BB) information and useExpand
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Particle Swarm Optimization With Recombination and Dynamic Linkage Discovery
In this paper, we try to improve the performance of the particle swarm optimizer by incorporating the linkage concept, which is an essential mechanism in genetic algorithms, and design a new linkageExpand
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A Survey of Linkage Learning Techniques in Genetic and Evolutionary Algorithms
This paper reviews and summarizes existing linkage learning techniques for genetic and evolutionary algorithms in the literature. It first introduces the definition of linkage in both biologicalExpand
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Introducing recombination with dynamic linkage discovery to particle swarm optimization
In this paper, we introduce the recombination operator with the technique of dynamic linkage discovery to particle swarm optimization (PSO) in order to improve the performance of PSO. NumericalExpand
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Analysis of particle interaction in particle swarm optimization
In this paper, we analyze the behavior of particle swarm optimization (PSO) on the facet of particle interaction. We firstly propose a statistical interpretation of particle swarm optimization inExpand
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Enabling the Extended Compact Genetic Algorithm for Real-Parameter Optimization by Using Adaptive Discretization
An adaptive discretization method, called split-on-demand (SoD), enables estimation of distribution algorithms (EDAs) for discrete variables to solve continuous optimization problems. SoD randomlyExpand
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