000			02190nam a22003373a 4500
001			UPMIN-00003211627
003			UPMIN
005			20230117170250.0
008			221017b |||||||| |||| 00| 0 eng d
040			_aDLC _cUPMin _dupmin
041			_aeng
090		0	_aLG993.5 2008 _bC6 L33
100			_aLacsamana, Margareth Ann Gallo _91399
245			_aModified shuffled frog-leaping algorithm (SFLA) applied to cutting stock problem / _cMargareth Ann Gallo Lacsamana.
260			_c2008
300			_a90 leaves.
502			_aThesis (BS Computer Science) -- University of the Philippines Mindanao, 2008
520	3		_aThis study focused on solving the one-dimensional variant of CSP with single stock length and without contiguity. In this study, an evolutionary algorithm (EA) called the shuffled frog-leaping algorithm (SFLA) was applied to the CSP. However, the local search of the SFLA was first modified using an operator from the genetic algorithm called the uniform crossover so s to use it in solving the CSP. The modified SFLA (MSLFA) with crossover was the only main algorithm used. The evaluation of the efficiency of the MSFLA was limited only to the chosen benchmark test problems. The experimental results in terms of the probability of convergence to a global optimal solution imply that the MSFLA can be an efficient tool for solving combinatorial optimization problems comparable to the EP. Generally, MSFLA provided equal results with EP in one of the benchmark test problems and an almost at par results with the EP in the other test problems with very little percentage error. Accordingly, MSFLA is a promising approach in solving the cutting stock problem if further improvements can be done.
650	1	7	_aCutting stock problem. _91400
650	1	7	_aCombinatorial optimization. _91401
650	1	7	_aOptimization. _9733
650	1	7	_aDiscrete optimization. _91377
650	1	7	_aEvolutionary algorithms. _91378
650	1	7	_aGenetic algorithm. _9344
650	1	7	_aShuffled frog leaping algorithm. _91396
650	1	7	_aUniform crossover. _91331
658			_aUndergraduate Thesis _cCMSC200, _2BSCS
905			_aFi
905			_aUP
942			_2lcc _cTHESIS
999			_c2224 _d2224