Genetic algorithm phd thesis

Genetic algorithm phd thesis

optimal solutions to the timetabling problem. Genetic algorithms, based on Darwin's theory of evolution is one such method. The aim of this study is to optimize a general university course scheduling process based on genetic algorithms using some defined blogger.com by: 3 Tiryakioglu, Seyma, "Multiobjective Reservoir Optimization Using Genetic Algorithm" (). Electronic Theses and Dissertations. blogger.com This Thesis is brought to you for free and open access by the Graduate School at eGrove. It has been accepted for Even Genetic Algorithm Phd Thesis when there is no one around to help you, there is a way out. Search for Genetic Algorithm Phd Thesis it on the Web, as there are plenty of Genetic Algorithm Phd Thesis websites that offer online homework help. Thousands of students made their choice and trusted their grades on homework writing services

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Click here to sign up. Download Free PDF, genetic algorithm phd thesis. Deborah Carvalho. Download Download PDF Full PDF Package Download Full PDF Package This Paper, genetic algorithm phd thesis.

A short summary of this paper. Carvalho Alex A. Freitas Pontificia Universidade Catolica do Parana PUCPR Pontificia Universidade Catolica do Parana PUCPR Postgraduate genetic algorithm phd thesis in applied computer science Postgraduate program in applied computer science R. Imaculada Conceicao, Curitiba — PR R.

Curitiba — PR Brazil Brazil alex ppgia. Comendador Franco, Curitiba-PR Brazil deborah utp. Hence, at first glance, small disjuncts are not important, genetic algorithm phd thesis, since they tend to be error prone.

However, a deeper study of the issue of small disjuncts reveals that in fact they are The problem of small disjuncts is a serious quite interesting in the context of data mining, for the challenge for data mining algorithms.

In essence, following reasons: small disjuncts are rules covering a small number of examples. Due to their nature, genetic algorithm phd thesis, small a Although each disjunct covers a small number of disjuncts tend to be error prone and contribute to examples, the set of all small disjuncts can cover a large a decrease in predictive accuracy.

This paper number of examples. The basic idea is that examples Therefore, if the rule induction algorithm ignores small belonging to large disjuncts are classified by disjuncts and discovers only large disjuncts, classification rules produced by a decision-tree algorithm, accuracy will be significantly degraded.

while examples belonging to small disjuncts b Some small disjuncts cover examples that represent whose classification is considerably more rare cases in the application domain, which constitutes an difficult are classified by rules produced by a interesting concept to be discovered. Actually, genetic algorithm phd thesis, bearing in genetic algorithm specifically designed for this mind that one of the goals of data mining is to discover task.

The basic idea is that representation is intuitive for the user, genetic algorithm phd thesis. From a logical examples belonging to large disjuncts are classified by viewpoint, typically the discovered genetic algorithm phd thesis are in disjunctive rules produced by a decision-tree algorithm, while normal form, where each rule represents a genetic algorithm phd thesis and examples belonging to small disjuncts whose each rule condition represents a conjunct.

A small classification is considerably more difficult are classified disjunct can be defined as a rule which covers a small by rules produced by a new genetic algorithm, number of training examples Holte et al. specifically designed for discovering small-disjunct rules. In general rule induction algorithms have a bias that The rest of this paper is organized as follows.

Section 2 favors the discovery of large disjuncts, rather than small discusses related work. Section 3 describes our hybrid disjuncts, genetic algorithm phd thesis.

This section assumes that the reader is familiar with known decision-tree algorithm to classify examples decision-trees, which is a very well-known knowledge belonging to large disjuncts and use a new genetic discovery paradigm in data mining, and focus on a algorithm to discover rules classifying examples detailed description of a new genetic algorithm proposed belonging to small disjuncts.

This approach tries to in this paper. Section 4 reports the results of experiments combine the best of both worlds. Decision-tree algorithms evaluating the performance of the proposed method on a have a bias towards generality that is well suited for large case study dataset.

Finally, section 5 presents the disjuncts, but not for small disjuncts. On the other hand, conclusions and some future research directions. genetic algorithms are robust, flexible algorithms which tend to cope well with attribute interactions Freitas 2 RELATED WORKNoda et al.

Holte et al. In the first phase we run C4. In effect, this corresponds decision tree induction algorithm Quinlan The to genetic algorithm phd thesis all small disjuncts, regardless of their induced, genetic algorithm phd thesis, pruned tree is transformed into a set of rules in estimated performance. b Eliminating only the small the usual way - that is, each path from the root to a leaf disjuncts whose estimated performance is poor. c Using node corresponds to a rule predicting the class specified a specificity bias for small disjuncts while using a in the corresponding leaf node.

Hence, a decision tree generality bias for large disjuncts. The third approach with d leaves is transformed into a rule set with d rules or turned out to be partly but not entirely successful.

His method consists examples covered by the rule is smaller than or equal to a of using a genetic algorithm phd thesis algorithm to cope with large given threshold.

disjuncts and an instance-based learning IBL algorithm to cope with small disjuncts. The basic idea of this hybrid The second phase consists of using a genetic algorithm to method is that IBL algorithms have a specificity bias, discover rules covering the examples belonging to small which should be more suitable for coping with small disjuncts.

We have developed a new genetic algorithm for disjuncts. In a high level of abstraction, the basic idea of this phase, which will be described in detail below. For each test example, disadvantage that the IBL algorithm does not discover any we first check whether the example is covered by some high-level, genetic algorithm phd thesis rules.

By contrast, we use a large-disjunct rule. If so, the example is classified by the genetic algorithm that does discover high-level, corresponding rule, genetic algorithm phd thesis, which is one of the rules induced by comprehensible small-disjunct rules, which is important the decision tree algorithm.

Otherwise the example is in the context of data mining. classified by some small-disjunct rule, which is one of the Weiss investigated the interaction of noise with rules discovered by our genetic algorithm. rare cases true exceptions and showed that this It should be noted that the small-disjunct rules discovered interaction led to degradation in classification accuracy by our genetic algorithm can overlap each other.

when small-disjunct rules are eliminated. However, these Therefore, if a test example is to be classified by some results have a limited utility in practice, since the analysis small disjunct rule, there might be one of the following of this interaction was made possible by using artificially two kinds of rule conflict.

generated data set. In real-world data genetic algorithm phd thesis the correct First, there might be more than one small-disjunct rule concept to be discovered is not known a priori, so that it is covering the test example. If this is the case, the example not possible to make a clear distinction between noise and is classified by the highest-quality rule among all small- true rare cases.

Weiss did experiments showing disjunct rules covering the examples. The quality of a genetic algorithm phd thesis that, genetic algorithm phd thesis, when noise is added to real-world data sets, small is measured by the value of the fitness function computed disjuncts contribute disproportionaly and significantly for by the genetic algorithm - described in section 3.

the total number of classification errors made by the Second, there might be no small-disjunct rule covering discovered rules. the test example. If this is the case, the example is classified by a default rule. A similar procedure is also used in several rule induction GENETIC-ALGORITHM METHOD algorithms. FOR RULE DISCOVERY Finally, note also that these kinds of rule conflict cannot As mentioned in the introduction, we propose a hybrid occur if the test example is to be classified by a large- method for rule discovery that combines decision trees disjunct rule, since these rules have mutually exclusive and genetic algorithms.

conditions of a rule antecedent. In our GA the minimum number of rule conditions is 2. Although this number 3. It is very unlikely that a rule with a SMALL-DISJUNCTS RULES single condition can accurately predict the class of an In this section genetic algorithm phd thesis describe our genetic algorithm GA example belonging to a small disjunct, so a lower limit of developed for discovering small-disjunct rules - i.

rules 2 seems to make sense. covering the examples in leaf nodes of a decision tree The maximum number of rule conditions is more difficult considered to be a small disjunct, as explained above. to determine. In principle, the maximum number of rule The first step in the design of a GA for rule discovery is conditions could be m, where m is the number of predictor to decide what an individual candidate solution attributes in the dataset.

However, this would have two represents. In our case, each individual represents a small- disadvantages. First, it could lead to the discovery of very disjunct rule. The genome of an individual consists of the long rules, genetic algorithm phd thesis, which goes against the desire to discover conditions in the antecedent IF part of the rule. The goal comprehensible rules, genetic algorithm phd thesis. Second, it would require a long of the GA is to evolve rule conditions that maximize the genome to represent individuals, which tends to increase predictive accuracy of the rule, as evaluated by a fitness processing time.

To avoid these problems, we use a measure - described below. The consequent THEN part heuristics to select the subset of attributes that is used to of the rule, which specifies the predicted class, is not compose rule conditions. represented in the genome. Rather, it is fixed for a given Our heuristics is based on the fact that different small GA run, so that all individuals have the same rule disjuncts identified by the decision-tree algorithm can consequent during all that run.

have several rule conditions in common. For instance, Each run of our GA discovers a single rule the best suppose that two sibling leaf nodes of the decision tree individual of the last generation predicting a given class were deemed small disjuncts and let k be the number of for examples belonging to a given small disjunct.

Since ancestor nodes of these two leaf nodes. Then the two we need to discover several rules to cover examples of corresponding rule antecedents have k - 1 conditions in several classes in several different small disjuncts, we run common. Therefore, it does not make much sense to use our GA several times for a given dataset.

Rather, genetic algorithm phd thesis, for each small disjunct, genetic algorithm phd thesis, the genome of a of small disjuncts and c is the number of classes to be GA individual contains only attributes that were not used predicted. At first glance this is a computationally expensive To represent a variable-length rule antecedent approach for rule discovery.

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