A Multi-population Approach to Dynamic Optimization Problems
By J. Branke, T. Kaußler, C.G.K. Smidt, Hartmut Schmeck. 2000
405 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/88f295cee32c77bac91626a5713e0cc71ba133f0
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In Artificial Intelligence
In Computational Theory and Mathematics
Citations in the corpus
- Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach (2006, methodology): “Other approaches, such as the Self-Organizing Scouts by Branke et al [9], separate the population into groups with specific functions of either tracking an optimum or exploring for new solutions.”
- Experimental study on population-based incremental learning algorithms for dynamic optimization problems (2005, positive): “Branke [7], [8] has grouped them into four categories: 1) increasing diversity after a change, such as the hypermutation scheme [9], [24]; 2) maintaining diversity throughout the run, such as the random immigrants scheme [15]; 3) memory-based methods, such as the diploidy and multiploidy approaches ”
- Transfer Learning-Based Dynamic Multiobjective Optimization Algorithms (2016, methodology): “[7] proposed the self-organizing scouts method, and this method used a major population to search for optimal solution and if the major population finds a peak, then a new population would be generated to track the change of this new peak.”
- A survey on evolutionary computation for complex continuous optimization (2021, methodology): “Based on the heterogeneous model, Branke et al. (2000) proposed the self-organizing scouts (SOS) algorithm. The SOS includes a parent population and many child populations. The parent population constantly explores and searches for new optima in the entire search space, while child populations gener”
- Evolutionary dynamic optimization: A survey of the state of the art (2012, methodology): “To separate the sub-populations, SOS [141] confines each sub-population to a hyper-cube determined by a center (the most fit individual in the population) and a pre-defined range.”
- A Clustering Particle Swarm Optimizer for Locating and Tracking Multiple Optima in Dynamic Environments (2010, methodology): “into traditional EAs to address DOPs [8], [18], [44], including diversity schemes [13], [15], [43], memory schemes [6], [42], [48], multipopulation schemes [ 7 ], [47], adaptive schemes [29], [45], [46], multiobjective optimization methods [11], and problem change detecting approaches [35].”