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Linkage in Evolutionary Computation
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Gewicht:
910 g
Format:
241x160x33 mm
Beschreibung:
Presents recent results in Linkage in Evolutionary Computation
Models and Theories.- Parallel Bivariate Marginal Distribution Algorithm with Probability Model Migration.- Linkages Detection in Histogram-Based Estimation of Distribution Algorithm.- Linkage in Island Models.- Real-Coded ECGA for Solving Decomposable Real-Valued Optimization Problems.- Linkage Learning Accuracy in the Bayesian Optimization Algorithm.- The Impact of Exact Probabilistic Learning Algorithms in EDAs Based on Bayesian Networks.- Linkage Learning in Estimation of Distribution Algorithms.- Operators and Frameworks.- Parallel GEAs with Linkage Analysis over Grid.- Identification and Exploitation of Linkage by Means of Alternative Splicing.- A Clustering-Based Approach for Linkage Learning Applied to Multimodal Optimization.- Studying the Effects of Dual Coding on the Adaptation of Representation for Linkage in Evolutionary Algorithms.- Symbiotic Evolution to Avoid Linkage Problem.- EpiSwarm, a Swarm-Based System for Investigating Genetic Epistasis.- Real-Coded Extended Compact Genetic Algorithm Based on Mixtures of Models.- Applications.- Genetic Algorithms for the Airport Gate Assignment: Linkage, Representation and Uniform Crossover.- A Decomposed Approach for the Minimum Interference Frequency Assignment.- Set Representation and Multi-parent Learning within an Evolutionary Algorithm for Optimal Design of Trusses.- A Network Design Problem by a GA with Linkage Identification and Recombination for Overlapping Building Blocks.- Knowledge-Based Evolutionary Linkage in MEMS Design Synthesis.
In recent years, the issue of linkage in GEAs has garnered greater attention and recognition from researchers. Conventional approaches that rely much on ad hoc tweaking of parameters to control the search by balancing the level of exploitation and exploration are grossly inadequate. As shown in the work reported here, such parameters tweaking based approaches have their limits; they can be easily "fooled" by cases of triviality or peculiarity of the class of problems that the algorithms are designed to handle. Furthermore, these approaches are usually blind to the interactions between the decision variables, thereby disrupting the partial solutions that are being built up along the way.