Minimisation of multioutput Reed-Muller binary decision diagrams using hybrid genetic algorithm

1995 ◽  
Vol 31 (20) ◽  
pp. 1722-1723 ◽  
Author(s):  
A.E.A. Almaini ◽  
N. Zhuang ◽  
F. Bourset
Keyword(s):  
Genetic Algorithm ◽  
Binary Decision Diagrams ◽  
Hybrid Genetic Algorithm ◽  
Decision Diagrams ◽  
Binary Decision

scholarly journals Genetic algorithm for binary and functional decision diagrams optimization

2018 ◽  
Vol 31 (2) ◽  
pp. 169-187
Author(s):  
Stojkovic Suzana ◽  
Velickovic Darko ◽  
Moraga Claudio
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Data Structure ◽  
Optimization Problems ◽  
Binary Decision Diagrams ◽  
Size Optimization ◽  
Decision Diagrams ◽  
Crossover Operator ◽  
Binary Decision ◽  
Discrete Functions

Decision diagrams (DD) are a widely used data structure for discrete functions representation. The major problem in DD-based applications is the DD size minimization (reduction of the number of nodes), because their size is dependent on the variables order. Genetic algorithms are often used in different optimization problems including the DD size optimization. In this paper, we apply the genetic algorithm to minimize the size of both Binary Decision Diagrams (BDDs) and Functional Decision Diagrams (FDDs). In both cases, in the proposed algorithm, a Bottom-Up Partially Matched Crossover (BU-PMX) is used as the crossover operator. In the case of BDDs, mutation is done in the standard way by variables exchanging. In the case of FDDs, the mutation by changing the polarity of variables is additionally used. Experimental results of optimization of the BDDs and FDDs of the set of benchmark functions are also presented.

Optimizing Binary Decision Diagrams for Interpretable Machine Learning Classification

2021 ◽  
Author(s):  
Gianpiero Cabodi ◽  
Paolo E. Camurati ◽  
Alexey Ignatiev ◽  
Joao Marques-Silva ◽  
Marco Palena ◽  
...  
Keyword(s):  
Machine Learning ◽  
Binary Decision Diagrams ◽  
Decision Diagrams ◽  
Binary Decision ◽  
Machine Learning Classification ◽  
Interpretable Machine Learning
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