000			02210nam a22003133a 4500
001			UPMIN-00006023633
003			UPMIN
005			20230105135938.0
008			221014b |||||||| |||| 00| 0 eng d
040			_aDLC _cUPMin _dupmin
041			_aeng
090		0	_aLG 993.5 2011 _bC6 E24
100			_aEbero, Ria Theresa Magnaye. _9940
245			_aShuffled frog leaping-tabu search algorithm with grouping genetic algorithm operators applied on cutting stock problem (CSP) / _cRia Theresa Magnaye Ebero.
260			_c2011
300			_a106 leaves.
502			_aThesis (BS Computer Science) -- University of the Philippines Mindanao, 2011
520	3		_aThis study was done to solve the one-dimensional variant of CSP with single stock length and without contiguity. The shuffled frog leaping algorithm (SFLA) was applied to CSP but was modified by embedding the tabu search (TS) in the SFLA's local search. The grouping genetic algorithm (GGA) operators were also used to address a constraint in SFLA. GGA crossover was used to create a new solution i SFLA while GGA mutation was used to create a new solution in TS. The efficiency of the algorithm was evaluated by using the chosen test data used by Lacsaman's (2008) study, namely the modified shuffled frog leaping algorithm (MSFLA). There were 8 parameter settings used in the study, 4 sets for SFLA and 2 sets for TS. The first parameter set of SFLA was from Amiri et al (2009) while the other set was generated by the proponent. The results showed that, generally, SFLA-TS performed better compared to MSFLA. It provided better results compared to MSFLA on test data 1,4 and 5, similar results with MSFLA in test data 2, and equal results on test data 3. Generally, SFLA-TS can be a promising solution in solving CSP.
650	1	7	_aShuffled Frog Leaping Algorithm (SFLA) _9941
650	1	7	_aCutting Stock Problem CSP) _9942
650	1	7	_aTabu Search (TS) _9943
650	1	7	_aGrouping Genetic Algorithm (GGA) _9944
650	1	7	_aHybrid algorithm. _9945
650	1	7	_aModified Shuffled Leaping Algorithm (MSFLA) _9946
658			_aUndergraduate Thesis _cCMSC200, _2BSCS
905			_aFi
905			_aUP
942			_2lcc _cTHESIS
999			_c2700 _d2700