EOBLExtensiveness of Blank Lines
EOBLEd Ott Baseball League (fantasy baseball)
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References in periodicals archive ?
Before introducing the EOBL, we should firstly explain opposition-based learning (OBL) [44].
Since the opposition solution of OBL generated may not be more favorable than the current search space to search the global optimal solution, thus in this paper, we can use elite opposition-based learning (EOBL) strategy.
The EOBL generates the opposition population according to the elite individual and evaluates the current population and the elite population at the same time; in addition, it makes full use of the characteristics of the elite individuals to contain more useful search information than the ordinary individuals which improve the diversity of the population to certain extent.
The reason behind keeping the individual that violates any constraint of the constraint conditions is that each iteration of the population has to implement the elite opposition-based learning (EOBL) strategy and propagation operation; then the individual that violates any constraint may satisfy the constraints in the next iteration.
Elite opposition-based learning (EOBL) [48] is based on the elite step leader using OBL principle to generate elite opposition-based population to participate in competitive evolution, so as to improve population diversity of LSA.
According to the above principles, the EOBL procedures are as follows.
Two strategies of SM and EOBL are added to the LSA to balance the exploitation and exploration of the algorithm; the flowchart of the LSA-SM is shown in Figure 4.