Papers by Abdel-Rahman Hedar
Electronics, 2022
The foundation of machine learning is to enable computers to automatically solve certain problems... more The foundation of machine learning is to enable computers to automatically solve certain problems. One of the main tools for achieving this goal is genetic programming (GP), which was developed from the genetic algorithm to expand its scope in machine learning. Although many studies have been conducted on GP, there are many questions about the disruption effect of the main GP breeding operators, i.e., crossover and mutation. Moreover, this method often suffers from high computational costs when implemented in some complex applications. This paper presents the meta-heuristics programming framework to create new practical machine learning tools alternative to the GP method. Furthermore, the immune system programming with local search (ISPLS) algorithm is composed from the proposed framework to enhance the classical artificial immune system algorithm with the tree data structure to deal with machine learning applications. The ISPLS method uses a set of breeding procedures over a tree s...
In this paper, we present a new approach of hybrid simulated annealing method for minimizing mult... more In this paper, we present a new approach of hybrid simulated annealing method for minimizing multimodel functions called the simulated annealing heuristic pattern search (SAHPS) method. Two subsidiary methods are proposed to achieve the final form of the global search method SAHPS. First, we introduce the approximate descent direction (ADD) method, which is a derivative-free procedure with high ability of producing a descent direction. Then, the ADD method is combined with a pattern search method with direction pruning to construct the heuristic pattern search (HPS) method. The last method is hybridized with simulated annealing to obtain the SAHPS method. The experimental results through well-known test functions are shown to demonstrate the efficiency of the proposed method SAHPS.
The interface between computer science and operations research has drawn much attention recently ... more The interface between computer science and operations research has drawn much attention recently especially in optimization which is a main tool in operations research. In optimiza-tion area, the interest on this interface has rapidly increased in the last few years in order to develop nonstandard algorithms that can deal with optimization problems which the stan-dard optimization techniques often fail to deal with. Global optimization problems represent a main category of such problems. Global optimization refers to finding the extreme value of a given nonconvex function in a certain feasible region and such problems are classified in two classes; unconstrained and constrained problems. Solving global optimization prob-lems has made great gain from the interest in the interface between computer science and operations research. In general, the classical optimization techniques have difficulties in dealing with global optimization problems. One of the main reasons of their failure is...
Normalised fuzzy index for research ranking
Behaviour & Information Technology, 2018
There are great interests of designing research metrics and indices to measure the research impac... more There are great interests of designing research metrics and indices to measure the research impacts in research institutes. Unfortunately, most of those indices ignore critical design issues, e.g. the disparity between domains, the impact of journals or conferences in which papers are published, normalising the range of the index values to certain intervals, and the scalability of using the index to rank different research entities. In this paper, a new normalised fuzzy index, (NFindex), is proposed as a fuzzy-based research impact metric. The proposed index is a scalable index whose values are normalised to the percentage levels. NFindex achieves both inter-discipline normalisation and intra-discipline consistency. The capability of NFindex to achieve the inter-discipline normalisation enables fair comparison between different research domains regardless their nature in terms of influence and contribution to other research areas, e.g. natural science. Therefore, NFindex gives a uni...
The core of artificial intelligence and machine learning is to get computers to solve problems au... more The core of artificial intelligence and machine learning is to get computers to solve problems automatically. One of the great tools that attempt to achieve that goal is Genetic Programming (GP). GP is a generalization procedure of the well-known meta-heuristic of Genetic Algorithms (GAs). Meta-heuristics have shown successful performance in solving many combinatorial search problems. In this paper, we introduce a more general framework of meta-heuristics called MetaHeuristics Programming (MHP) as general machine learning tools. As an alternative to GP, Tabu Programming (TP) is proposed as a special procedure of MHP frameworks. One of the main features of MHP is to exploit local search in order to overcome some drawbacks of GP, especially high disruption of its main operations; crossover and mutation. We show the efficiency of the proposed TP method through numerical experiments.
The standard versions of Evolutionary Algorithms (EAs) have two main drawbacks: unlearned termina... more The standard versions of Evolutionary Algorithms (EAs) have two main drawbacks: unlearned termination criteria and slow convergence. Although several attempts have been made to modify the original versions of Evolutionary Algorithms (EAs), only very few of them have considered the issue of their termination criteria. In general, EAs are not learned with automatic termination criteria, and they cannot decide when or where they can terminate. On the other hand, there are several successful modifications of EAs to overcome their slow convergence. One of the most effective modifications is Memetic Algorithms. In this paper, we modify genetic algorithm (GA), as an example of EAs, with new termination criteria and acceleration elements. The proposed method is called GA with Automatic Accelerated Termination (G3AT). In the G3AT method, Gene Matrix (GM) is constructed to equip the search process with a self-check to judge how much exploration has been done. Moreover, a special mutation oper...
On common fixed point theorems of compatible mappings in Menger spaces
Demonstratio Mathematica. Warsaw Technical University Institute of Mathematics, 1998
Scatter Search for Simulation-Based Optimization
2017 International Conference on Computer and Applications (ICCA), 2017
In this paper, the scatter search is modified and combined with the variable-sample technique to ... more In this paper, the scatter search is modified and combined with the variable-sample technique to deal with the simulation-based optimization problem. First, a new design of scatter search is proposed to deal with the deterministic global optimization problem. Then, the variable-sample technique is combined with the modified scatter search method in order to compose a new global search method that can deal with simulation-based optimization problem. Several numerical experiments have been simulated to show the efficiency of the proposed methods.
Defective software modules cause software failures, in-crease development and maintenance costs, ... more Defective software modules cause software failures, in-crease development and maintenance costs, and decrease customer satisfaction. Understanding the impact of defects on various business applications is an essential way to improve software quality. For instance, we would expect that functionality bugs are fixed faster than other types of bugs due to their critical nature. Prior researches have often treated all bugs as similar when studying various aspects of software quality which is not a good way. Identifying bug category before debugging will lead to better handling to select proper destination to fix it. In this paper, ways to automatically predicting different bug types will be presented by using natural language mining techniques such as K Nearest Neighbor and Naive Bayes. Evaluating prediction techniques will be based on precision and recall measures . We achieved the following recall and precision respectively 91% and 75% for standard related issues, 79% and 75% for function related issues, 79% and 73 % for user interface related issues and 72% and 79% for logic related issues, 79% is the highest accuracy achieved with Naive Bayes classifier.
Adaptive Memory Matrices for Automatic Termination of Evolutionary Algorithms
Evolutionary Algorithms (EAs) still have no auto- matic termination criterion. In this paper, we ... more Evolutionary Algorithms (EAs) still have no auto- matic termination criterion. In this paper, we modify a genetic algorithm (GA), as an example of EAs, with new automatic termination criteria and acceleration elements. The proposed method is called the GA with Gene and Landmark Matrices (GAGLM). In the GAGLM method, the Gene Matrix (GM) and Landmark Matrix (LM) are constructed to equip the search process with a self-check to judge how much exploration has been done and to maintain the population diversity. Moreover, a special mutation operation called "Mutagenesis" is defined to achieve more efficient and faster exploration and exploitation processes. The computational experiments show the efficiency of the GAGLM method, especially its new elements of the mutagenesis operation and the proposed termination criteria.
Wireless Sensor Networks Management Using Differential Evolution and Minimum Dominating Sets
2021 International Telecommunications Conference (ITC-Egypt), 2021
Challenges of managing and controlling wireless sensor networks (WSNs) includes efficient routing... more Challenges of managing and controlling wireless sensor networks (WSNs) includes efficient routing, increasing sensor node lifetime by conserving energy consumption, and base stations fault-tolerance. These challenges can be solved by help of Minimum Dominating Set (MDS) algorithms. By applying MDS to WSNs, only base stations (dominating sensors) take the burden of the communication instead of all sensors, and consequently saving the communication bandwidth, energy consumption, and increase the network lifetime. In addition to that, having multiple MDS improve base stations fault tolerance by scheduling different MDS periodically. In this research, a new Differential Evolutionary (DE) is composed to solve the general MDS problem. First, this problem is formulated as a binary optimization problem. Then, new evolutionary operations are introduced to fit the considered problem and wireless sensor network applications. Beside these evolutionary operations, other search enhancement operations are invoked to improve the search diversification and intensification process in addition to enabling the search process to find several distinct solutions. Those solutions can help in scheduling the network control nodes in order to increase its lifetime. Several experimental simulations over benchmark networks are carried out to test the efficiency of the proposed method. The results demonstrate the quality of the proposed method for obtaining minimum dominating sets for the considered test networks.
Solar radiation prediction is an important process in ensuring optimal exploitation of solar ener... more Solar radiation prediction is an important process in ensuring optimal exploitation of solar energy power. Numerous models have been applied to this problem, such as numerical weather prediction models and artificial intelligence models. However, well-designed hybridization approaches that combine numerical models with artificial intelligence models to yield a more powerful model can provide a significant improvement in prediction accuracy. In this paper, we propose novel hybrid machine learning approaches that exploit auxiliary numerical data. The proposed hybrid methods invoke different machine learning paradigms, including feature selection, classification, and regression. Additionally, numerical weather prediction (NWP) models are used in the proposed hybrid models. Feature selection is used for feature space dimension reduction to reduce the large number of recorded parameters that affect estimation and prediction processes. The rough set theory is applied for attribute reducti...
Applied Sciences, 2020
Generating practical methods for simulation-based optimization has attracted a great deal of atte... more Generating practical methods for simulation-based optimization has attracted a great deal of attention recently. In this paper, the estimation of distribution algorithms are used to solve nonlinear continuous optimization problems that contain noise. One common approach to dealing with these problems is to combine sampling methods with optimal search methods. Sampling techniques have a serious problem when the sample size is small, so estimating the objective function values with noise is not accurate in this case. In this research, a new sampling technique is proposed based on fuzzy logic to deal with small sample sizes. Then, simulation-based optimization methods are designed by combining the estimation of distribution algorithms with the proposed sampling technique and other sampling techniques to solve the stochastic programming problems. Moreover, additive versions of the proposed methods are developed to optimize functions without noise in order to evaluate different efficienc...
Sensors, 2020
In wireless sensor/ad hoc networks, all wireless nodes frequently flood the network channel by tr... more In wireless sensor/ad hoc networks, all wireless nodes frequently flood the network channel by transmitting control messages causing “broadcast storm problem”. Thus, inspired by the physical backbone in wired networks, a Virtual Backbone (VB) in wireless sensor/ad hoc networks can help achieve efficient broadcasting. A well-known and well-researched approach for constructing virtual backbone is solving the Connected Dominating Set (CDS) problem. Furthermore, minimizing the size of the CDS is a significant research issue. We propose a new parallel scatter search algorithm with elite and featured cores for constructing a wireless sensor/ad hoc network virtual backbones based on finding minimum connected dominating sets of wireless nodes. Also, we addressed the problem of VB node/nodes failure by either deploying a previously computed VBs provided by the main pSSEF algorithm that does not contain the failed node/nodes, or by using our proposed FT-pSSEF algorithm repairing the broken VB...
Algorithms, 2020
Simulated annealing is a well-known search algorithm used with success history in many search pro... more Simulated annealing is a well-known search algorithm used with success history in many search problems. However, the random walk of the simulated annealing does not benefit from the memory of visited states, causing excessive random search with no diversification history. Unlike memory-based search algorithms such as the tabu search, the search in simulated annealing is dependent on the choice of the initial temperature to explore the search space, which has little indications of how much exploration has been carried out. The lack of exploration eye can affect the quality of the found solutions while the nature of the search in simulated annealing is mainly local. In this work, a methodology of two phases using an automatic diversification and intensification based on memory and sensing tools is proposed. The proposed method is called Simulated Annealing with Exploratory Sensing. The computational experiments show the efficiency of the proposed method in ensuring a good exploration ...
Computation, Mar 18, 2020
With the rapid growth of simulation software packages, generating practical tools for simulation-... more With the rapid growth of simulation software packages, generating practical tools for simulation-based optimization has attracted a lot of interest over the last decades. In this paper, a modified method of Estimation of Distribution Algorithms (EDAs) is constructed by a combination with variable-sample techniques to deal with simulation-based optimization problems. Moreover, a new variable-sample technique is introduced to support the search process whenever the sample sizes are small, especially in the beginning of the search process. The proposed method shows efficient results by simulating several numerical experiments.
Algorithms, 2020
An efficient routing using a virtual backbone (VB) network is one of the most significant improve... more An efficient routing using a virtual backbone (VB) network is one of the most significant improvements in the wireless sensor network (WSN). One promising method for selecting this subset of network nodes is by finding the minimum connected dominating set (MCDS), where the searching space for finding a route is restricted to nodes in this MCDS. Thus, finding MCDS in a WSN provides a flexible low-cost solution for the problem of event monitoring, particularly in places with limited or dangerous access to humans as is the case for most WSN deployments. In this paper, we proposed an adaptive scatter search (ASS-MCDS) algorithm that finds the near-optimal solution to this problem. The proposed method invokes a composite fitness function that aims to maximize the solution coverness and connectivity and minimize its cardinality. Moreover, the ASS-MCDS methods modified the scatter search framework through new local search and solution update procedures that maintain the search objectives. ...
Mathematical and Computational Applications, 2020
Enhancing Evolutionary Algorithms (EAs) using mathematical elements significantly contribute to t... more Enhancing Evolutionary Algorithms (EAs) using mathematical elements significantly contribute to their development and control the randomness they are experiencing. Moreover, the automation of the primary process steps of EAs is still one of the hardest problems. Specifically, EAs still have no robust automatic termination criteria. Moreover, the highly random behavior of some evolutionary operations should be controlled, and the methods should invoke advanced learning process and elements. As follows, this research focuses on the problem of automating and controlling the search process of EAs by using sensing and mathematical mechanisms. These mechanisms can provide the search process with the needed memories and conditions to adapt to the diversification and intensification opportunities. Moreover, a new quadratic coding and quadratic search operator are invoked to increase the local search improving possibilities. The suggested quadratic search operator uses both regression and Ra...
Mathematics, 2019
In this paper, we target the problems of finding a global minimum of nonlinear and stochastic pro... more In this paper, we target the problems of finding a global minimum of nonlinear and stochastic programming problems. To solve this type of problem, we propose new approaches based on combining direct search methods with Evolution Strategies (ESs) and Scatter Search (SS) metaheuristics approaches. First, we suggest new designs of ESs and SS with a memory-based element called Gene Matrix (GM) to deal with those type of problems. These methods are called Directed Evolution Strategies (DES) and Directed Scatter Search (DSS), respectively, and they are able to search for a global minima. Moreover, a faster convergence can be achieved by accelerating the evolutionary search process using GM, and in the final stage we apply the Nelder-Mead algorithm to find the global minimum from the solutions found so far. Then, the variable-sample method is invoked in the DES and DSS to compose new stochastic programming techniques. Extensive numerical experiments have been applied on some well-known fun...
Algorithms, 2018
We propose a novel method for adaptive K-means clustering. The proposed method overcomes the prob... more We propose a novel method for adaptive K-means clustering. The proposed method overcomes the problems of the traditional K-means algorithm. Specifically, the proposed method does not require prior knowledge of the number of clusters. Additionally, the initial identification of the cluster elements has no negative impact on the final generated clusters. Inspired by cell cloning in microorganism cultures, each added data sample causes the existing cluster ‘colonies’ to evaluate, with the other clusters, various merging or splitting actions in order for reaching the optimum cluster set. The proposed algorithm is adequate for clustering data in isolated or overlapped compact spherical clusters. Experimental results support the effectiveness of this clustering algorithm.
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Papers by Abdel-Rahman Hedar