Brudaru, Ocatv; Furdu, Iulian; Ebendt, Rüdiger Embryonic genetic algorithm with random generational growing strategy for optimizing variable ordering of BDDS. (English) Zbl 1265.90345 Sci. Stud. Res., Ser. Math. Inform. 20, No. 1, 45-60 (2010). Summary: This paper addresses the problem of optimizing the variable ordering in binary decision diagrams (BDDs). A new hybrid embryonic genetic algorithm is proposed for optimizing the variable ordering that combines a branch-and-bound technique with the basic genetic algorithm. It uses fitness based on a lower bound and embryos instead of full chromosomes. A novel growing technique introduces two new growing operators. The results of an experimental evaluation illustrate the efficiency of the approach. MSC: 90C59 Approximation methods and heuristics in mathematical programming 68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) 68W20 Randomized algorithms Keywords:BDD; OBDD optimization; GA PDFBibTeX XMLCite \textit{O. Brudaru} et al., Sci. Stud. Res., Ser. Math. Inform. 20, No. 1, 45--60 (2010; Zbl 1265.90345)