Khaled Rasheed

Stock market prediction with multiple classifiers

Stock market prediction is attractive and challenging. According to the efficient market hypothesis, stock prices should follow a random walk pattern and thus should not be predictable with more than about 50 percent accuracy. In this paper, we investigated the predictability of the Dow Jones Industrial Average index to show that not all periods are equally random. We used the Hurst exponent to select a period with great predictability. Parameters for generating training patterns were determined heuristically by auto-mutual information and false nearest neighbor methods. Some inductive machine-learning classifiers—artificial neural network, decision tree, and k-nearest neighbor were then trained with these generated patterns. Through appropriate collaboration of these models, we achieved prediction accuracy up to 65 percent.

A genetic algorithm for continuous design space search

Genetic algorithms (GAs) have been extensively used as a means for performing global optimization in a simple yet reliable manner. However, in some realistic engineering design optimization domains the simple, classical implementation of a GA based on binary encoding and bit mutation and crossover is often inefficient and unable to reach the global optimum. In this paper we describe a GA for continuous design space optimization that uses new GA operators and strategies tailored to the structure and properties of engineering design domains. Empirical results in the domains of supersonic transport aircraft and supersonic missile inlets demonstrate that the newly formulated GA can be significantly better than the classical GA in both efficiency and reliability.

MDGA: motif discovery using a genetic algorithm

Computationally identifying transcription factor binding sites in the promoter regions of genes is an important problem in computational biology and has been under intensive research for a decade. To predict the binding site locations efficiently, many algorithms that incorporate either approximate or heuristic techniques have been developed. However, the prediction accuracy is not satisfactory and binding site prediction thus remains a challenging problem. In this paper, we develop an approach that can be used to predict binding site motifs using a genetic algorithm. Based on the generic framework of a genetic algorithm, the approach explores the search space of all possible starting locations of the binding site motifs in different target sequences with a population that undergoes evolution. Individuals in the population compete to participate in the crossovers and mutations occur with a certain probability. Initial experiments demonstrated that our approach could achieve high prediction accuracy in a small amount of computation time. A promising advantage of our approach is the fact that the computation time does not explicitly depend on the length of target sequences and hence may not increase significantly when the target sequences become very long.

Constrained multi-objective optimization using steady state genetic algorithms

In this paper we propose two novel approaches for solving constrained multi-objective optimization problems using steady state GAs. These methods are intended for solving real-world application problems that have many constraints and very small feasible regions. One method called Objective Exchange Genetic Algorithm for Design Optimization (OEGADO) runs several GAs concurrently with each GA optimizing one objective and exchanging information about its objective with the others. The other method called Objective Switching Genetic Algorithm for Design Optimization (OSGADO) runs each objective sequentially with a common population for all objectives. Empirical results in benchmark and engineering design domains are presented. A comparison between our methods and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) shows that our methods performed better than NSGA-II for difficult problems and found Pareto-optimal solutions in fewer objective evaluations. The results suggest that our methods are better applicable for solving real-world application problems wherein the objective computation time is large.

High-Performance Supersonic Missile Inlet Design Using Automated Optimization

A multilevel design strategy for supersonic missile inlet design is developed. The multilevel design strategy combines an efe cient simple physical model analysis tool and a sophisticated computational e uid dynamics (CFD) Navier ‐ Stokes analysis tool. The efe cient simple analysis tool is incorporated into the optimization loop, and the sophisticated CFD analysis tool is used to verify, select, and e lter the e nal design. The genetic algorithms and multistart gradient line search optimizers are used to search the nonsmooth design space. A geometry model for the supersonic missile inlet is developed. A supersonic missile inlet that starts at Mach 2.6 and cruises at Mach 4 was designed. Signie cant improvement of the inlet total pressure recovery has been obtained. Detailed e owe eld analysis is also presented.

Decision Tree and Ensemble Learning Algorithms with Their Applications in Bioinformatics

Machine learning approaches have wide applications in bioinformatics, and decision tree is one of the successful approaches applied in this field. In this chapter, we briefly review decision tree and related ensemble algorithms and show the successful applications of such approaches on solving biological problems. We hope that by learning the algorithms of decision trees and ensemble classifiers, biologists can get the basic ideas of how machine learning algorithms work. On the other hand, by being exposed to the applications of decision trees and ensemble algorithms in bioinformatics, computer scientists can get better ideas of which bioinformatics topics they may work on in their future research directions. We aim to provide a platform to bridge the gap between biologists and computer scientists.

Multiobjective GA optimization using reduced models

In this paper, we propose a novel method for solving multiobjective optimization problems using reduced models. Our method, called objective exchange genetic algorithm for design optimization (OEGADO), is intended for solving real-world application problems. For such problems, the number of objective evaluations performed is a critical factor as a single objective evaluation can be quite expensive. The aim of our research is to reduce the number of objective evaluations needed to find a well-distributed sampling of the Pareto-optimal region by applying reduced models to steady-state multiobjective GAs. OEGADO runs several GAs concurrently with each GA optimizing one objective and forming a reduced model of its objective. At regular intervals, each GA exchanges its reduced model with the others. The GAs use these reduced models to bias their search toward compromise solutions. Empirical results in several engineering and benchmark domains comparing OEGADO with two state-of-the-art multiobjective evolutionary algorithms show that OEGADO outperformed them for difficult problems.

A Survey of Fitness Approximation Methods Applied in Evolutionary Algorithms

Evolutionary algorithms (EAs) used in complex optimization domains usually need to perform a large number of fitness function evaluations in order to get near-optimal solutions. In real world application domains such as engineering design problems, such evaluations can be extremely computationally expensive. In some extreme cases there is no clear definition of the fitness function or the fitness function is too ambiguous to be deterministically evaluated. It is therefore common to estimate or approximate the fitness. A popular method is to construct a so-called surrogate or meta-model, which can simulate the behavior of the original fitness function, but can be evaluated much faster. An interesting trend is to use multiple surrogates to gain better performance in fitness approximation. In this chapter, an up-to-date survey of fitness approximation applied in evolutionary algorithms is presented. The main focus areas are the methods of fitness approximation, the working styles of fitness approximation, and the management of the approximation during the optimization process. To conclude, some open questions in this area are discussed.

Using surrogate modeling in the prediction of fibrinogen adsorption onto polymer surfaces.

We present a Surrogate (semiempirical) Model for prediction of protein adsorption onto the surfaces of biodegradable polymers that have been designed for tissue engineering applications. The protein used in these studies, fibrinogen, is known to play a key role in blood clotting. Therefore, fibrinogen adsorption dictates the performance of implants exposed to blood. The Surrogate Model combines molecular modeling, machine learning and an Artificial Neural Network. This novel approach includes an accounting for experimental error using a Monte Carlo analysis. Briefly, measurements of human fibrinogen adsorption were obtained for 45 polymers. A total of 106 molecular descriptors were generated for each polymer. Of these, 102 descriptors were computed using the Molecular Operating Environment (MOE) software based upon the polymer chemical structures, two represented different monomer types, and two were measured experimentally. The Surrogate Model was developed in two stages. In the first stage, the three descriptors with the highest correlation to adsorption were determined by calculating the information gain of each descriptor. Here a Monte Carlo approach enabled a direct assessment of the effect of the experimental uncertainty on the results. The three highest-ranking descriptors, defined as those with the highest information gain for the sample set, were then selected as the input variables for the second stage, an Artificial Neural Network (ANN) to predict fibrinogen adsorption. The ANN was trained using one-half of the experimental data set (the training set) selected at random. The effect of experimental error on predictive capability was again explored using a Monte Carlo analysis. The accuracy of the ANN was assessed by comparison of the predicted values for fibrinogen adsorption with the experimental data for the remaining polymers (the validation set). The mean value of the Pearson correlation coefficient for the validation data sets was 0.54 +/- 0.12. The average root-mean-square (relative) error in prediction for the validation data sets is 38%. This is an order of magnitude less than the range of experimental values (i.e., 366%) and compares favorably with the average percent relative standard deviation of the experimental measurements (i.e., 17.9%). The effects of each of the user-defined parameters in the ANN were explored. None were observed to have a significant effect on the results. Thus, the Surrogate Model can be used to accurately and unambiguously identify polymers whose fibrinogen absorption is at the limits of the range (i.e., low or high) which is an essential requirement for assessing polymers for regenerative tissue applications.

An incremental-approximate-clustering approach for developing dynamic reduced models for design optimization

This paper we describe a method for improving genetic algorithm based optimization using reduced models. The main idea is to maintain a large sample of the points encountered in the course of the optimization divided into clusters. Least squares quadratic approximations are periodically formed of the entire sample as well as the big enough clusters. These approximations are used as a reduced model to compute cheap approximations of the fitness function through a two phase approach in which the point is first classified (into potentially feasible, infeasible or unevaluable) and then its fitness is computed accordingly. We then use the reduced models to speedup the GA optimization by making the genetic operators such as mutation and crossover more informed. The proposed approach is particularly suitable for search spaces with expensive evaluation functions, such as those that arise in engineering design. Empirical results in several engineering design domains demonstrate that the proposed method can significantly speed up the GA optimizer.