ALGORITMA A GENETIK UNTUK MASALAH PROGRAM BILANGAN BULAT MULTl OBJEKTIF FUZZY, Genetik Algorithm for Fuzzy Multiobjective Integer Programming Problem - Diana, Diana; Subanar, Subanar (Sekolah Pascasarjana UGM, 2003)

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This research is aimed at finding •solutions in mult10bjccti ve integer programming pro blems. The solutions for mu l tiobjective programming problems is compromise solutions between objective functions, i.e., finding Pareto solutions. By considering the ambiguity of OM's judgments as a human, fuz:.y goals tor objective functions are incorporated After determined membership functions JJ,{x) for objective functions, assume that1-1v ••1, 2, ..., k. reflecting the aspiration level of the OM for each membership functions, the problems can be formulated as augmented minimax problem By solving the above augmented minimax problem a Pareto solution which is nearest to the reference membership levels can be obtained regardless of its un iquncss. Genetic algor.:hm is stochastic (random), searching method that mimic the metaphor of natural biological evolution, like natural selection, crossover, and mutation The rule of this game is the fittest will win In 1t's application, genetic algorithm just need t he evaluation function of the problem which will to optimised, not the mathematica lly guided algorithm is needed. In this paper, it will use genetic algorithm step by using intera ctive satisficing method. The result of genetic algorithm programming simulation proves that the increasing of fitness value will increase the minimized object1w funrlion value and .decrease the • •.;nimizcd objective function value. Th11 the genetic a l gorithm hds s u ccetued in finding comprom ise solut i ons for the fuzzy mu ltiobjective in t eger programming