A Monte-Carlo study of genetic algorithm initial population generation methods

Hill, Raymond R.

doi:10.1145/324138.324430

Cited by 16 publications

(10 citation statements)

References 4 publications

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“…When compared with randomly generated initial population, it has been shown that heuristically-generated initial populations can improve convergence speed and find better solutions [20], [22], [24], [41]. Based on these findings, DMEL first discovers a set of first-order rules and places it in the initial population.…”

Section: B Generating First-order Rulesmentioning

confidence: 99%

A novel evolutionary data mining algorithm with applications to churn prediction

Chan

Yao³

2003

IEEE Trans. Evol. Computat.

258

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Abstract-Classification is an important topic in data mining research. Given a set of data records, each of which belongs to one of a number of predefined classes, the classification problem is concerned with the discovery of classification rules that can allow records with unknown class membership to be correctly classified. Many algorithms have been developed to mine large data sets for classification models and they have been shown to be very effective. However, when it comes to determining the likelihood of each classification made, many of them are not designed with such purpose in mind. For this, they are not readily applicable to such problem as churn prediction. For such an application, the goal is not only to predict whether or not a subscriber would switch from one carrier to another, it is also important that the likelihood of the subscriber's doing so be predicted. The reason for this is that a carrier can then choose to provide special personalized offer and services to those subscribers who are predicted with higher likelihood to churn. Given its importance, we propose a new data mining algorithm, called data mining by evolutionary learning (DMEL), to handle classification problems of which the accuracy of each predictions made has to be estimated. In performing its tasks, DMEL searches through the possible rule space using an evolutionary approach that has the following characteristics: 1) the evolutionary process begins with the generation of an initial set of first-order rules (i.e., rules with one conjunct/condition) using a probabilistic induction technique and based on these rules, rules of higher order (two or more conjuncts) are obtained iteratively; 2) when identifying interesting rules, an objective interestingness measure is used; 3) the fitness of a chromosome is defined in terms of the probability that the attribute values of a record can be correctly determined using the rules it encodes; and 4) the likelihood of predictions (or classifications) made are estimated so that subscribers can be ranked according to their likelihood to churn. Experiments with different data sets showed that DMEL is able to effectively discover interesting classification rules. In particular, it is able to predict churn accurately under different churn rates when applied to real telecom subscriber data.

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Section: B Generating First-order Rulesmentioning

confidence: 99%

A novel evolutionary data mining algorithm with applications to churn prediction

Chan

Yao³

2003

IEEE Trans. Evol. Computat.

258

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“…The chromosomes are generated with random alleles. For a gene X; PðX ¼ 1Þ ¼ 0:5 (iii) Heuristic Multi-Gene (HMG) chromosomes: In this scheme, chromosomes in the initial population are formed with the greedy heuristic [5]. For a gene X; PðX ¼ 1Þ ¼ Active decision variables in greedy estimate/Total decision variables…”

Section: Initial Populationmentioning

confidence: 99%

“…In some problem instances the probability of generating feasible strings is zero. Hill [5] lists the probability of generating feasible strings for different problem instances.…”

Section: Introductionmentioning

confidence: 99%

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Tackling 0/1 knapsack problem with gene induction

Bhatia

Basu

2003

Soft Computing - A Fusion of Foundations, Methodologies and App

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We propose a gene induction approach for genetic algorithms. It is more robust compared to the traditional approach in genetic algorithms. The approach was applied to 0/1 knapsack problem. It found near optimal results in all the representative problem instances reported in the literature, while traditional approaches failed in a number of instances because of preponderance of infeasible individuals in the population. In combination with a heuristic mutation operator, our method provided better results for all the problem instances investigated.

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“…This, of course, depends on many factors, one of them being the initial population. Generally, a good initial population encourages the GA to converge faster and=or to better solutions, just as a good initial starting point helps a gradient-based nonlinear optimization algorithm to arrive at a better solution (Hill 1999). In this section, we will present the GA component of our hGA, and then proceed to explain how the SA technique is used to generate the initial population.…”

Section: The Hga For Ooc Constructionmentioning

confidence: 99%