Optimasi Derajat Keanggotaan Fuzzy Tsukamoto Menggunakan Algoritma Genetika Untuk Diagnosis Penyakit Sapi Potong

                Sistem inferensi fuzzy bisa digunakan untuk diagnosis penyakit pada sapi potong. Untuk mendapatkan akurasi yang tinggi maka batasan fungsi keanggotaan fuzzy perlu ditentukan secara tepat. Penggunaan metode logika fuzzy untuk memperoleh hasil diagnosis penyakit pada sapi potong sesuai pakar berdasarkan batasan gejala penyakit dan aturan-aturan yang diperoleh dari pakar. Batasan tersebut bisa diperbaiki menggunakan Algoritma Genetika untuk mendapatkan akurasi yang lebih baik. Pengujian yang dilakukan pada 51 data dari beberapa gejala penyakit menghasilkan akurasi sebesar 98,04% dengan menggunakan parameter genetika terbaik antara lain ukuran populasi sebesar 80, ukuran generasi sebesar 15, nilai Crossover rate (Cr) sebesar 0,9, dan nilai Mutation rate (Mr) sebesar 0,06. Akurasi tersebut mengalami peningkatan sebesar 3,54% sesudah dilakukannya optimasi pada metode logika fuzzy.Kata kunci: diagnosis penyakit sapi potong, logika fuzzy, Algoritma GenetikaAbstract                Fuzzy inference systems can be used to diagnose cattle disease. Prior to obtaining the most accurate of limitation, fuzzy membership functions must be defined precisely. Thus, the limits will be optimized along with Genetic Algorithm to get more accurate results. The function of fuzzy logic methods in the diagnosis of disease is relied upon the parametres set by experts. Tests that were performed on 51 data from some of the symptoms of the disease resulted in an accuracy of 98.04% using the best genetic parameters with the population size of 80, the size of the generation of 15, crossover rate value of 0.9, and the value of mutation rate of 0.06. The accuracy has increased by 3.54% compare to results before optimization. Keywords: cattle disease diagnosis, fuzzy logic, genetic algorithm

Evaluating genetic algorithms through the approximability hierarchy

Optimization problems frequently appear in any scientific domain. Most of the times, the corresponding decision problem turns out to be NP-hard, and in these cases genetic algorithms are often used to obtain approximated solutions. However, the difficulty to approximate different NP-hard problems can vary a lot. In this paper, we analyze the usefulness of using genetic algorithms depending on the approximation class the problem belongs to. In particular, we use the standard approximability hierarchy, showing that genetic algorithms are especially useful for the most pessimistic classes of the hierarchy

Development and optimization of aluminum nanocomposites for production of tribological elements

Rezime: Pri razvoju i proizvodnji novih materijala i elemenata eksperiment ima značajnu ulogu. Pristup eksperimentalnom istraživanju ne može se zamisliti bez upotrebe dizajna eksperimenta čijom primenom se pristupa sistematskom načinu planiranja eksperimenta, izvođenju i interpretaciji rezultata eksperimenata. Za potrebe ovog rada su razvijeni, a u radu su analizirani novi nanokompoziti sa A356 osnovom ojačani SiC i Al2O3 nanočesticama različitih veličina i sadržaja. U okviru ispitivanja određene su i prikazane fizičke i mehaničke karakteristike nanokompozita. U radu su sprovedena tribološka ispitivanja primenom dizajna eksperimenta za prvu seriju materijala koji do trenutka proizvodnje nisu bili predmet ranijih istraživanja. Nanokompoziti su proizvedeni sa malim masenim sadržajem ojačavajućih nanočestica primenom modifikovanog kompokasting procesa. Ostvareni eksperimentalni rezultati prvom serijom materijala ukazali su na pravac i tok razvoja nanokompozita sa novim sadržajem ojačavača. Druga faza istraživanja nanokompozita je usmerena na tribološka ispitivanja jer se prvom serijom materijala dokazalo da nije ostvareno značajno poboljšanje u mehaničkim i tribološkim karakteristikama nanokompozita. Izvršena je analiza pohabanih površina nanokompozita što je od velikog značaja za praktičnu primenu ovih materijala. Primenom optimizacionih metoda izvršena je višekriterijumska optimizacija i određena optimalna kombinacija faktora kojom se postižu najbolje karakteristike nanokompozita. Na osnovu eksperimentalnih istraživanja ostvarenih u ovoj disertaciji može se zaključiti da su ostvarena poboljšanja u mehaničkim i tribološkim karakteristikama nanokompozita u poređenju sa osnovnom legurom. Područje primene aluminijumskih nanokompozita neprekidno se širi s obzirom na kombinaciju svojstava koja se mogu postići dodavanjem različitih ojačavača. Dobijene karakteristike razvijenih nanokompozita omogućavaju njihovo korišćenje pri modeliranju i naponsku analizu različitih mašinskih elemenata u CAD softveru. Izvršena je numerička analiza zupčastih parova i ustanovljeno je da se maksimalne vrednosti ekvivalentnog napona javljaju u podnožju zubaca spregnutih zupčanika. Primenom nanokompozita za izradu zupčastog para može se postići veći prenos snage u odnosu na zupčasti par izrađen od osnovne legure, zatim smanjuje se pojava inicijalnih prslina, masa prenosnika, i nivo buke i vibracije u zupčastim prenosnicima manjih snaga, a povećava se njihova otpornost na habanje.Abstract: The experiment has a significant role in the development and production of new materials and machine elements. An approach to experimental research cannot be imagined without design of experiment usage, which repesents a systematic way of planning an experiment, performing and interpreting the experiments results. For the purposes of this thesis, new nanocomposites with A356 base reinforced with SiC and Al2O3 nanoparticles of different sizes and contents were developed and analyzed. Within this research, the physical and mechanical characteristics of nanocomposites were determined and presented. Tribological tests were performed using the design of experiment for the first series of materials that were not the subject of previous research, in today’s literature sources, until the time of production. Nanocomposites were produced with a low mass content of reinforcing nanoparticles using a modified compocasting process. The achieved experimental results with the first series of materials indicated the direction and course of development of nanocomposites with a new content of reinforcements. The second phase of nanocomposite research is focused on tribological tests because the first series of materials didn’t proved the significant improvement in the mechanical and tribological characteristics of nanocomposites is achieved. The analysis of worn surfaces of nanocomposites was performed, which is of great importance for the practical application of these materials. By applying optimization methods, multicriteria optimization was performed and the optimal combination of factors was determined for which gives the nanocomposites of the best characteristics. Based on the experimental research achieved in this dissertation, it can be concluded that improvements have been made both in the mechanical and tribological characteristics of nanocomposites compared to the base alloy. Application field of aluminum nanocomposites is constantly expanding due to the combination of properties that can be achieved by adding different reinforcements. The obtained characteristics of the developed nanocomposites enable their usage in modeling and stress analysis of various machine elements in CAD software. Stress analysis of gear pairs was performed and it was concluded that the maximum values of equivalent stress occur at the base of the teeth of the coupled gears. The use of nanocomposites for the production of gear pair can achieve a higher power transmission compared to the gear pair made of base alloy, then reduces the occurrence of initial cracks, gear mass, and noise and vibration levels in gears of lower power, and increases their wear resistance

Substructural Analysis Using Evolutionary Computing Techniques

Substructural analysis (SSA) was one of the very first machine learning techniques to be applied to chemoinformatics in the area of virtual screening. For this method, given a set of compounds typically defined by their fragment occurrence data (such as 2D fingerprints). The SSA computes weights for each of the fragments which outlines its contribution to the activity (or inactivity) of compounds containing that fragment. The overall probability of activity for a compound is then computed by summing up or combining the weights for the fragments present in the compound. A variety of weighting schemes based on specific relationship-bound equations are available for this purpose. This thesis identifies uplift to the effectiveness of SSA, using two evolutionary computation methods based on genetic traits, particularly the genetic algorithm (GA) and genetic programming (GP). Building on previous studies, it was possible to analyse and compare ten published SSA weighting schemes based on a simulated virtual screening experiment. The analysis showed the most effective weighting scheme to be the R4 equation which was a part of document-based weighting schemes. A second experiment was carried out to investigate the application of GA-based weighting scheme for the SSA in comparison to an experiment using the R4 weighting scheme. The GA algorithm is simple in concept focusing purely on suitable weight generation and effective in operation. The findings show that the GA-based SSA is superior to the R4-based SSA, both in terms of active compound retrieval rate and predictive performance. A third experiment investigated the genetic application via a GP-based SSA. Rigorous experiment results showed that the GP was found to be superior to the existing SSA weighting schemes. In general, however, the GP-based SSA was found to be less effective than the GA-based SSA. A final experimented is described in this thesis which sought to explore the feasibility of data fusion on both the GA and GP. It is a method producing a final ranking list from multiple sets of ranking lists, based on several fusion rules. The results indicate that data fusion is a good method to boost GA-and GP-based SSA searching. The RKP rule was considered the most effective fusion rule

The effects of two new crossover operators on genetic algorithm performance

WOS: 000281591300087In this study, two new crossover operators in genetic algorithm are proposed; sequential and random mixed crossover. In the first stage, existing and developed crossover operators were applied to two benchmark problems, namely the reinforced concrete beam problem and the space truss problem. In the second stage, the developed crossover operators were applied to a deep beam problem and, a concrete mix design problem. The fittest values obtained using developed crossover operators were higher than those were obtained with existing crossover operator after 30,000 generations. Moreover, in developed crossover operators, the random mixed crossover yields higher fitness values than the existing crossover operators. (c) 2010 Elsevier B. V. All rights reserved