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Multinational evolutionary algorithms
Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), 2003Since practical problems often are very complex with a large number of objectives, it can be difficult or impossible to create an objective function expressing all the criteria of good solutions. Sometimes a simpler function can be used where local optimas could be both valid and interesting.
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Geometry of evolutionary algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, 2011The various flavors of Evolutionary Algorithms look very similar when cleared of algorithmically irrelevant differences such as domain of application and phenotype interpretation. Representation-independent algorithmic characteristics like the selection scheme can be freely exchanged between algorithms.
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An Evolutionary Clustering Algorithm
2004There are many heuristic algorithms for clustering, from which the most important are the hierarchical methods of agglomeration, especially the Ward’s method. Among the iterative methods the most universally used is the C–means method and it’s generalizations.
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A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization
IEEE Transactions on Evolutionary Computation, 2016Yuan Yuan, Xin Yao
exaly
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation, 2007Hui Li, Qingfu Zhang
exaly
The networked evolutionary algorithm: A network science perspective
Applied Mathematics and Computation, 2018Matjaz Perc, Xianbin Cao, Ke Tang
exaly
A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective Optimization
IEEE Transactions on Evolutionary Computation, 2017Shouyong Jiang, Shengxiang Yang
exaly