IMPLEMENTASI GENETIC ALGORITHM (GA) BERBASIS STRATEGI PENALTI PADA KNAPSACK PROBLEM

Riska Malinda , 1117032052 (2015) IMPLEMENTASI GENETIC ALGORITHM (GA) BERBASIS STRATEGI PENALTI PADA KNAPSACK PROBLEM. FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM, UNIVERSITAS LAMPUNG.

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Abstrak

Permasalahan optimasi sering kali ditemukan pada kehidupan sehari-hari, seperti permasalahan investasi, penentuan rute pendistribusian barang dan penentuan jadwal piket di rumah sakit, dll. Permasalahan investasi termasuk kedalam Knapsack Problem (KP). KP merupakan permasalahan optimasi kombinatorik dan permasalahan NP-Hard. Berbagai penelitian telah dilakukan dan melaporkan beberapa metode konvensional yang dianggap mampu untuk menyelesaikan permasalahan KP. Meskipun demikian, beberapa metode konvensional masih sangat sulit untuk mendapatkan solusi optimal dengan waktu yang relatif cepat karena KP merupakan permasalahan NP-Hard. Maka, pada penelitian ini dikembangkan suatu model GA sebagai salah satu metode heuristik untuk menyelesaikanKP. GA merupakan algoritma yang dikembangkan meniru proses evolusi alamiah pada makhluk hidup. Tahap-tahap pencarian solusi optimal pada GA yaitu inisialisasi, evaluasi, pindah silang, mutasi dan seleksi. Proses inisialisasi dilakukan dengan dua tipe pembangkitan kandidat solusi. Pertama, kandidat solusi dibangkitkan langsung pada ruang solusi yang layak (directedGA).Kedua, kandidat solusi dibangkitkan secara acak (randomly GA). Strategi penalti sebagai metode untuk mengevaluasi kandidat solusi dan menangani kendala menjadi fokus pada penelitian ini. Uji coba dilakukan pada test problem yang ada pada literatur. Hasil penelitian mencatat strategi penalti dengan directed GA lebih cocokdibandingkandenganrandomlyGA. KataKunci:GeneticAlgorithm(GA),KnapsackProblem(KP),Strategi Penalti. ABSTRACT Optimization problems have many function in a real life, such as for select investments, for determine the distribution of goods and for determine the picket schedules in the hospitals, etc. Investment issues are included into the knapsack problem. Knapsack problem is a combinatorial optimization problem that belongs to the NP-Hard problem. Some of conventional methods have been reportedtosolveit.However several methodsare still veryhard toget an optimal solution with a relatively quick time, because the knapsack problem belongs to the NP-hard problem. Therefore, in this paper we developed a model of GA as heuristicsmethodtosolveknapsack. GAis an algorithm developed similar to the natural evolutionary process. Stages at the GAinclude initialitation,evaluation, crossover,mutationand selection.The initialization process was done by two types of generate candidate solutions. The first,generating ofcandicatesolution directlyonthefeasible space (directedGA). The second, generating of candidate solution randomly (randomly GA). Penalty strategy asa method forevaluating the candidate solutionand handling constraint will be focus onthis research. Experiment was done by usingtest problems given in the literature. Based on the experiments, penalty strategy with the directed GA isbettersuitedthantherandomlyGA. Keywords:GeneticAlgorithm(GA),KnapsackProblem(KP),PenaltyStrategy.

Tipe Karya Ilmiah: Skripsi
Subyek: Q Science (General) > QA Mathematics > QA76 Computer software
Program Studi: Fakultas MIPA > Prodi Ilmu Komputer
Depositing User: 4331588 . Digilib
Date Deposited: 23 Sep 2015 04:20
Last Modified: 23 Sep 2015 04:20
URI: http://digilib.unila.ac.id/id/eprint/12925

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