Search results for: Genetic Evolution

Authors: Ludovic Favre, Thibaut M. Schafer, Jean-Luc Robyr, Elena-Lavinia Niederhäuser

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This paper presents an optimization method based on genetic algorithm for the energy management inside buildings developed in the frame of the project Smart Living Lab (SLL) in Fribourg (Switzerland). This algorithm optimizes the interaction between renewable energy production, storage systems and energy consumers. In comparison with standard algorithms, the innovative aspect of this project is the extension of the smart regulation over three simultaneous criteria: the energy self-consumption, the decrease of greenhouse gas emissions and operating costs. The genetic algorithm approach was chosen due to the large quantity of optimization variables and the non-linearity of the optimization function. The optimization process includes also real time data of the building as well as weather forecast and users habits. This information is used by a physical model of the building energy resources to predict the future energy production and needs, to select the best energetic strategy, to combine production or storage of energy in order to guarantee the demand of electrical and thermal energy. The principle of operation of the algorithm as well as typical output example of the algorithm is presented.

Keywords: Building’s energy, control system, energy management, modelling, genetic optimization algorithm, renewable energy, greenhouse gases, energy storage.

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Authors: Wenqin Wang, Shouming Zhong

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This paper presents the robust stability criteria for uncertain genetic regulatory networks with time-varying delays. One key point of the criterion is that the decomposition of the matrix ˜D into ˜D = ˜D1 + ˜D2. This decomposition corresponds to a decomposition of the delayed terms into two groups: the stabilizing ones and the destabilizing ones. This technique enables one to take the stabilizing effect of part of the delayed terms into account. Meanwhile, by choosing an appropriate new Lyapunov functional, a new delay-dependent stability criteria is obtained and formulated in terms of linear matrix inequalities (LMIs). Finally, numerical examples are presented to illustrate the effectiveness of the theoretical results.

Keywords: Genetic regulatory network, Time-varying delay, Uncertain system, Lyapunov-Krasovskii functional

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Authors: N. Kh. Gomidze, I. N. Jabnidze, K. A. Makharadze

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The influence of inhomogeneities of plasma and statistical characteristics on the propagation of signal is very actual in wireless communication systems. While propagating in the media, the deformation and evaluation of the signal in time and space take place and on the receiver we get a deformed signal. The present article is dedicated to studying the space-time evolution of rectangular, sinusoidal, exponential and bi-exponential impulses via numerical experiment in the collisional, cold plasma. The presented method is not based on the Fourier-presentation of the signal. Analytically, we have received the general image depicting the space-time evolution of the radio impulse amplitude that gives an opportunity to analyze the concrete results in the case of primary impulse.

Keywords: Collisional, cold plasma, rectangular pulse signal, impulse envelope.

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Authors: Shahla Shoghian, Maryam Kouzehgar

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The main objective of this paper is applying a comparison between the Wolf Pack Search (WPS) as a newly introduced intelligent algorithm with several other known algorithms including Particle Swarm Optimization (PSO), Shuffled Frog Leaping (SFL), Binary and Continues Genetic algorithms. All algorithms are applied on two benchmark cost functions. The aim is to identify the best algorithm in terms of more speed and accuracy in finding the solution, where speed is measured in terms of function evaluations. The simulation results show that the SFL algorithm with less function evaluations becomes first if the simulation time is important, while if accuracy is the significant issue, WPS and PSO would have a better performance.

Keywords: Wolf Pack Search, Particle Swarm Optimization, Continues Genetic Algorithm, Binary Genetic Algorithm, Shuffled Frog Leaping, Optimization.

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Authors: Ganesh. Vulasala, Sivanagaraju. Sirigiri, Ramana. Thiruveedula

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This paper presents an efficient approach to feeder reconfiguration for power loss reduction and voltage profile imprvement in unbalanced radial distribution systems (URDS). In this paper Genetic Algorithm (GA) is used to obtain solution for reconfiguration of radial distribution systems to minimize the losses. A forward and backward algorithm is used to calculate load flows in unbalanced distribution systems. By simulating the survival of the fittest among the strings, the optimum string is searched by randomized information exchange between strings by performing crossover and mutation. Results have shown that proposed algorithm has advantages over previous algorithms The proposed method is effectively tested on 19 node and 25 node unbalanced radial distribution systems.

Keywords: Distribution system, Load flows, Reconfiguration, Genetic Algorithm.

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Authors: K. Vijayalakshmi, S. Radhakrishnan

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In this paper we have proposed a novel dynamic least cost multicast routing protocol using hybrid genetic algorithm for IP networks. Our protocol finds the multicast tree with minimum cost subject to delay, degree, and bandwidth constraints. The proposed protocol has the following features: i. Heuristic local search function has been devised and embedded with normal genetic operation to increase the speed and to get the optimized tree, ii. It is efficient to handle the dynamic situation arises due to either change in the multicast group membership or node / link failure, iii. Two different crossover and mutation probabilities have been used for maintaining the diversity of solution and quick convergence. The simulation results have shown that our proposed protocol generates dynamic multicast tree with lower cost. Results have also shown that the proposed algorithm has better convergence rate, better dynamic request success rate and less execution time than other existing algorithms. Effects of degree and delay constraints have also been analyzed for the multicast tree interns of search success rate.

Keywords: Dynamic Group membership change, Hybrid Genetic Algorithm, Link / node failure, QoS Parameters.

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Authors: Mohd Herwan Sulaiman, Omar Aliman, Siti Rafidah Abdul Rahim

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This paper proposes a new methodology for the optimal allocation and sizing of Embedded Generation (EG) employing Real Coded Genetic Algorithm (RCGA) to minimize the total power losses and to improve voltage profiles in the radial distribution networks. RCGA is a method that uses continuous floating numbers as representation which is different from conventional binary numbers. The RCGA is used as solution tool, which can determine the optimal location and size of EG in radial system simultaneously. This method is developed in MATLAB. The effect of EG units- installation and their sizing to the distribution networks are demonstrated using 24 bus system.

Keywords: Embedded generation (EG), load flow study, optimal allocation, real coded genetic algorithm (RCGA).

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Authors: Sana Bouzaida, Anis Sakly, Faouzi M'Sahli

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In this paper, a TSK-type Neuro-fuzzy Inference System that combines the features of fuzzy sets and neural networks has been applied for the identification of MIMO systems. The procedure of adapting parameters in TSK model employs a Shuffled Frog Leaping Algorithm (SFLA) which is inspired from the memetic evolution of a group of frogs when seeking for food. To demonstrate the accuracy and effectiveness of the proposed controller, two nonlinear systems have been considered as the MIMO plant, and results have been compared with other learning methods based on Particle Swarm Optimization algorithm (PSO) and Genetic Algorithm (GA).

Keywords: Identification, Shuffled frog Leaping Algorithm (SFLA), TSK-type neuro-fuzzy model.

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Authors: Vichai Rungreunganaun, Chirawat Woarawichai

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The objective of this research is to calculate the optimal inventory lot-sizing for each supplier and minimize the total inventory cost which includes joint purchase cost of the products, transaction cost for the suppliers, and holding cost for remaining inventory. Genetic algorithms (GAs) are applied to the multi-product and multi-period inventory lot-sizing problems with supplier selection under storage space. Also a maximum storage space for the decision maker in each period is considered. The decision maker needs to determine what products to order in what quantities with which suppliers in which periods. It is assumed that demand of multiple products is known over a planning horizon. The problem is formulated as a mixed integer programming and is solved with the GAs. The detailed computation results are presented.

Keywords: Genetic Algorithms, Inventory lot-sizing, Supplier selection, Storage space.

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Authors: R. K. Agrawal, Rajni Bala

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Developing an accurate classifier for high dimensional microarray datasets is a challenging task due to availability of small sample size. Therefore, it is important to determine a set of relevant genes that classify the data well. Traditionally, gene selection method often selects the top ranked genes according to their discriminatory power. Often these genes are correlated with each other resulting in redundancy. In this paper, we have proposed a hybrid method using feature ranking and wrapper method (Genetic Algorithm with multiclass SVM) to identify a set of relevant genes that classify the data more accurately. A new fitness function for genetic algorithm is defined that focuses on selecting the smallest set of genes that provides maximum accuracy. Experiments have been carried on four well-known datasets1. The proposed method provides better results in comparison to the results found in the literature in terms of both classification accuracy and number of genes selected.

Keywords: Gene selection, genetic algorithm, microarray datasets, multi-class SVM.

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Authors: Truong Quang Dang Khoa, Masahiro Nakagawa

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In this paper, a novel associative memory model will be proposed and applied to memory retrievals based on the conventional continuous time model. The conventional model presents memory capacity is very low and retrieval process easily converges to an equilibrium state which is very different from the stored patterns. Genetic Algorithms is well-known with the capability of global optimal search escaping local optimum on progress to reach a global optimum. Based on the well-known idea of Genetic Algorithms, this work proposes a heuristic rule to make a mutation when the state of the network is trapped in a spurious memory. The proposal heuristic associative memory show the stored capacity does not depend on the number of stored patterns and the retrieval ability is up to ~ 1.

Keywords: Artificial Intelligent, Soft Computing, NeuralNetworks, Genetic Algorithms, Hopfield Neural Networks, andAssociative Memories.

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Authors: Orose Rugchati, Kanita Thanacharoenchanaphas

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This research studied the simulation of increased ambient ozone to estimate nutrient content and genetic changes in two Thai soybean cultivars (Chiang Mai 60 and Srisumrong 1). Ozone stress conditions affected proteins and lipids. It was found that proteins decreased, but lipids increased. Srisumrong 1 cultivars were more sensitive to ozone stress than Chiang Mai 60 cultivars. The effect of ozone stress conditions on plant phenotype and genotype was analyzed using the AFLP technique for the 2 Thai soybean cultivars (Chiang Mai 60 and Srisumrong 1).

Keywords: simulation, ambient ozone estimate, nutrient content, genetic changes , Thai soybean

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Authors: Salinee Thumronglaohapun

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The proper number and appropriate locations of service centers can save cost, raise revenue and gain more satisfaction from customers. Establishing service centers is high-cost and difficult to relocate. In long-term planning periods, several factors may affect the service. One of the most critical factors is uncertain demand of customers. The opened service centers need to be capable of serving customers and making a profit although the demand in each period is changed. In this work, the capacitated location-allocation problem with stochastic demand is considered. A mathematical model is formulated to determine suitable locations of service centers and their allocation to maximize total profit for multiple planning periods. Two heuristic methods, a local search and genetic algorithm, are used to solve this problem. For the local search, five different chances to choose each type of moves are applied. For the genetic algorithm, three different replacement strategies are considered. The results of applying each method to solve numerical examples are compared. Both methods reach to the same best found solution in most examples but the genetic algorithm provides better solutions in some cases.

Keywords: Location-allocation problem, stochastic demand, local search, genetic algorithm.

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Authors: Tomohiro Hachino, Kenji Shimoda, Hitoshi Takata

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This paper presents a method of model selection and identification of Hammerstein systems by hybridization of the genetic algorithm (GA) and particle swarm optimization (PSO). An unknown nonlinear static part to be estimated is approximately represented by an automatic choosing function (ACF) model. The weighting parameters of the ACF and the system parameters of the linear dynamic part are estimated by the linear least-squares method. On the other hand, the adjusting parameters of the ACF model structure are properly selected by the hybrid algorithm of the GA and PSO, where the Akaike information criterion is utilized as the evaluation value function. Simulation results are shown to demonstrate the effectiveness of the proposed hybrid algorithm.

Keywords: Hammerstein system, identification, automatic choosing function model, genetic algorithm, particle swarm optimization.

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Authors: Labiba Gilani, Asifullah Khan, Anwar M. Mirza

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This paper introduces a technique of distortion estimation in image watermarking using Genetic Programming (GP). The distortion is estimated by considering the problem of obtaining a distorted watermarked signal from the original watermarked signal as a function regression problem. This function regression problem is solved using GP, where the original watermarked signal is considered as an independent variable. GP-based distortion estimation scheme is checked for Gaussian attack and Jpeg compression attack. We have used Gaussian attacks of different strengths by changing the standard deviation. JPEG compression attack is also varied by adding various distortions. Experimental results demonstrate that the proposed technique is able to detect the watermark even in the case of strong distortions and is more robust against attacks.

Keywords: Blind Watermarking, Genetic Programming (GP), Fitness Function, Discrete Cosine Transform (DCT).

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Authors: C. Rajan, N. Shanthi, C. Rasi Priya, K. Geetha

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Routing in MANET is extremely challenging because of MANETs dynamic features, its limited bandwidth, frequent topology changes caused by node mobility and power energy consumption. In order to efficiently transmit data to destinations, the applicable routing algorithms must be implemented in mobile ad-hoc networks. Thus we can increase the efficiency of the routing by satisfying the Quality of Service (QoS) parameters by developing routing algorithms for MANETs. The algorithms that are inspired by the principles of natural biological evolution and distributed collective behavior of social colonies have shown excellence in dealing with complex optimization problems and are becoming more popular. This paper presents a survey on few meta-heuristic algorithms and naturally-inspired algorithms.

Keywords: Ant colony optimization, genetic algorithm, Naturally-inspired algorithms and particle swarm optimization.

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Authors: Mohd Sazli Saad, Hishamuddin Jamaluddin, Intan Zaurah Mat Darus

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This paper presents the development of an active vibration control using direct adaptive controller to suppress the vibration of a flexible beam system. The controller is realized based on linear parametric form. Differential evolution optimisation algorithm is used to optimize the controller using single objective function by minimizing the mean square error of the observed vibration signal. Furthermore, an alternative approach is developed to systematically search for the best controller model structure together with it parameter values. The performance of the control scheme is presented and analysed in both time and frequency domain. Simulation results demonstrate that the proposed scheme is able to suppress the unwanted vibration effectively.

Keywords: flexible beam, finite difference method, active vibration control, differential evolution, direct adaptive controller

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Authors: Zahra Khalid, Gul Muhammad Khan, Arbab Masood Ahmad

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Cartesian Genetic Programming (CGP) is explored to design an optimal circuit capable of early stage breast cancer detection. CGP is used to evolve simple multiplexer circuits for detection of malignancy in the Fine Needle Aspiration (FNA) samples of breast. The data set used is extracted from Wisconsins Breast Cancer Database (WBCD). A range of experiments were performed, each with different set of network parameters. The best evolved network detected malignancy with an accuracy of 99.14%, which is higher than that produced with most of the contemporary non-linear techniques that are computational expensive than the proposed system. The evolved network comprises of simple multiplexers and can be implemented easily in hardware without any further complications or inaccuracy, being the digital circuit.

Keywords: Breast cancer detection, cartesian genetic programming, evolvable hardware, fine needle aspiration (FNA).

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Authors: Öznur Özge Özcan, Canan Sercan, Hamza Kulaksız, Mesut Karahan, Korkut Ulucan

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Genetic structure is very important to understand the brain dopamine system which is related to athletic performance. Hopefully, there will be enough studies about athletics performance in the terms of addiction-related genetic markers in the future. In the present study, we intended to investigate the Receptor-2 Gene (DRD2) rs1800497, which is related to brain dopaminergic system. 10 sprinter and 10 endurance athletes were enrolled in the study. Real-Time Polymerase Chain Reaction method was used for genotyping. According to results, A1A1, A1A2 and A2A2 genotypes in athletes were 0 (%0), 3 (%15) and 17 (%85). A1A1 genotype was not found and A2 allele was counted as the dominating allele in our cohort. These findings show that dopaminergic mechanism effects on sport genetic may be explained by the polygenic and multifactorial view.

Keywords: Addiction, athletic performance, genotype, polymorphism, sport genetics.

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Authors: Karim Kemih, Yacine Yaiche, Malek Benslama

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To establish optical communication between any two satellites, the transmitter satellite must track the beacon of the receiver satellite and point the information optical beam in its direction. Optical tracking and pointing systems for free space suffer during tracking from high-amplitude vibration because of background radiation from interstellar objects such as the Sun, Moon, Earth, and stars in the tracking field of view or the mechanical impact from satellite internal and external sources. The vibrations of beam pointing increase the bit error rate and jam communication between the two satellites. One way to overcome this problem is the use of very small transmitter beam divergence angles of too narrow divergence angle is that the transmitter beam may sometimes miss the receiver satellite, due to pointing vibrations. In this paper we propose the use of genetic algorithm to optimize the BER as function of transmitter optics aperture.

Keywords: Optical Satellite Communication, Genetic Algorithm, Transmitter Optics Aperture

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Authors: G. Van Dijck, M. M. Van Hulle, M. Wevers

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A genetic algorithm (GA) based feature subset selection algorithm is proposed in which the correlation structure of the features is exploited. The subset of features is validated according to the classification performance. Features derived from the continuous wavelet transform are potentially strongly correlated. GA-s that do not take the correlation structure of features into account are inefficient. The proposed algorithm forms clusters of correlated features and searches for a good candidate set of clusters. Secondly a search within the clusters is performed. Different simulations of the algorithm on a real-case data set with strong correlations between features show the increased classification performance. Comparison is performed with a standard GA without use of the correlation structure.

Keywords: Classification, genetic algorithm, hierarchicalagglomerative clustering, wavelet transform.

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Authors: Bilal Alatas, Ahmet Arslan

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The main goal of data mining is to extract accurate, comprehensible and interesting knowledge from databases that may be considered as large search spaces. In this paper, a new, efficient type of Genetic Algorithm (GA) called uniform two-level GA is proposed as a search strategy to discover truly interesting, high-level prediction rules, a difficult problem and relatively little researched, rather than discovering classification knowledge as usual in the literatures. The proposed method uses the advantage of uniform population method and addresses the task of generalized rule induction that can be regarded as a generalization of the task of classification. Although the task of generalized rule induction requires a lot of computations, which is usually not satisfied with the normal algorithms, it was demonstrated that this method increased the performance of GAs and rapidly found interesting rules.

Keywords: Classification rule mining, data mining, genetic algorithms.

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Authors: Latha Parthiban, R. Subramanian

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Heart disease (HD) is a major cause of morbidity and mortality in the modern society. Medical diagnosis is an important but complicated task that should be performed accurately and efficiently and its automation would be very useful. All doctors are unfortunately not equally skilled in every sub specialty and they are in many places a scarce resource. A system for automated medical diagnosis would enhance medical care and reduce costs. In this paper, a new approach based on coactive neuro-fuzzy inference system (CANFIS) was presented for prediction of heart disease. The proposed CANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach which is then integrated with genetic algorithm to diagnose the presence of the disease. The performances of the CANFIS model were evaluated in terms of training performances and classification accuracies and the results showed that the proposed CANFIS model has great potential in predicting the heart disease.

Keywords: CANFIS, genetic algorithms, heart disease, membership function.

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Authors: Siva Prasad Darla, C. D. Naiju, K. Annamalai, Y. Upendra Sravan

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In recent years, environment regulation forcing manufactures to consider recovery activity of end-of- life products and/or return products for refurbishing, recycling, remanufacturing/repair and disposal in supply chain management. In this paper, a mathematical model is formulated for single product production-inventory system considering remanufacturing/reuse of return products and rate of return products follows a demand like function, dependent on purchasing price and acceptance quality level. It is useful in decision making to determine whether to go for remanufacturing or disposal of returned products along with newly produced products to satisfy a stationary demand. In addition, a modified genetic algorithm approach is proposed, inspired by particle swarm optimization method. Numerical analysis of the case study is carried out to validate the model.

Keywords: Genetic Algorithm, Particle Swarm Optimization, Production, Remanufacturing.

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Authors: Şt. Creangâ, V. Maciuc, A.V. Bâlteanu, S.S. Chelmu

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The paper presents a part of the results obtained in a complex research project on Romanian Grey Steppe breed, owner of some remarkable qualities such as hardiness, longevity, adaptability, special resistance to ban weather and diseases and included in the genetic fund (G.D. no. 822/2008.) from Romania. Following the researches effectuated, we identified alleles of six loci, codifying the six types of major milk proteins: alpha-casein S1 (α S1-cz); beta-casein (β-cz); kappa-casein (K-cz); beta-lactoglobulin (β-lg); alpha-lactalbumin (α-la) and alpha-casein S2 (α S2-cz). In system αS1-cz allele αs1-Cn B has the highest frequency (0.700), in system β-cz allele β-Cn A2 ( 0.550 ), in system K-cz allele k-CnA2 ( 0.583 ) and heterozygote genotype AB ( 0.416 ) and BB (0.375), in system β-lg allele β-lgA1 has the highest frequency (0.542 ) and heterozygote genotype AB ( 0.500 ), in system α-la there is monomorphism for allele α-la B and similarly in system αS2-cz for allele αs2-Cn A. The milk analysis by the isoelectric focalization technique (I.E.F.) allowed the identification of a new allele for locus αS1-casein, for two of the individuals under analysis, namely allele called αS1-casein IRV. When experiments were repeated, we noticed that this is not a proteolysis band and it really was a new allele that has not been registered in the specialized literature so far. We identified two heterozygote individuals, carriers of this allele, namely: BIRV and CIRV. This discovery is extremely important if focus is laid on the national genetic patrimony.

Keywords: allele, breed, genetic preservation, lactoproteins, Romanian Grey Steppe

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Authors: Jorge A. Ruiz-Vanoye, Ocotlán Díaz-Parra, Alejandro Fuentes-Penna, Daniel Vélez-Díaz, Edith Olaco García

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In this paper, we present the use of the discriminant analysis to select evolutionary algorithms that better solve instances of the vehicle routing problem with time windows. We use indicators as independent variables to obtain the classification criteria, and the best algorithm from the generic genetic algorithm (GA), random search (RS), steady-state genetic algorithm (SSGA), and sexual genetic algorithm (SXGA) as the dependent variable for the classification. The discriminant classification was trained with classic instances of the vehicle routing problem with time windows obtained from the Solomon benchmark. We obtained a classification of the discriminant analysis of 66.7%.

Keywords: Intelligent transportation systems, data-mining techniques, evolutionary algorithms, discriminant analysis, machine learning.

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Authors: R. Matousek, S. Lang, P. Minar, P. Pivonka

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In the control theory one attempts to find a controller that provides the best possible performance with respect to some given measures of performance. There are many sorts of controllers e.g. a typical PID controller, LQR controller, Fuzzy controller etc. In the paper will be introduced polynomial controller with novel tuning method which is based on the special pole placement encoding scheme and optimization by Genetic Algorithms (GA). The examples will show the performance of the novel designed polynomial controller with comparison to common PID controller.

Keywords: Evolutionary design, Genetic algorithms, PID controller, Pole placement, Polynomial controller

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Authors: A. Rattanapittayapron, O. Vanijajiva

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Canna indica is a prominent species complex in tropical and subtropical areas. They become indigenous in Southeast Asia where they have been introduced. At present, C. indica complex comprises over hundred hybrids, are cultivated as commercial horticulture. The species complex contains starchy rhizome having economic value in terms of food and herbal medicine. In addition, bright color of the flowers makes it a valuable ornamental plant and potential source for natural colorant. This study aims to assess genetic diversity of four varieties of C. indica complex based on SRAP (sequence-related amplified polymorphism) and iPBS (inter primer binding site) markers. We also examined phytochemical characteristics and antioxidant properties of the flower extracts from four different color varieties. Results showed that despite of the genetic variation, there were no significant differences in phytochemical characteristics and antioxidant properties of flowers. The SRAP and iPBS results agree with the more primitive traits showed by morphological information and phytochemical and antioxidant characteristics from the flowers. Since Canna flowers has long been used as natural colorants together with the antioxidant activities from the ethanol extracts in this study, there are likely to be good source for cosmetics additives.

Keywords: Canna indica, antioxidant activity, genetic diversity, SRAP, iPBS.

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Authors: Siva Prasad Darla, C. D. Naiju, K. Annamalai, S. S. Rajiv Sushanth

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Supply chain consists of all stages involved, directly or indirectly, includes all functions involved in fulfilling a customer demand. In two stage transportation supply chain problem, transportation costs are of a significant proportion of final product costs. It is often crucial for successful decisions making approaches in two stage supply chain to explicit account for non-linear transportation costs. In this paper, deterministic demand and finite supply of products was considered. The optimized distribution level and the routing structure from the manufacturing plants to the distribution centres and to the end customers is determined using developed mathematical model and solved by proposed particle swarm optimization based genetic algorithm. Numerical analysis of the case study is carried out to validate the model.

Keywords: Genetic Algorithm, Particle Swarm Optimization, Production, Remanufacturing

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Authors: Saleem Z. Ramadan, Gürsel A. Süer

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In this paper a genetic algorithm (GA) with dual-fitness function is proposed and applied to solve the deterministic identical machine scheduling problem. The mating fitness function value was used to determine the mating for chromosomes, while the selection fitness function value was used to determine their survivals. The performance of this algorithm was tested on deterministic identical machine scheduling using simulated data. The results obtained from the proposed GA were compared with classical GA and integer programming (IP). Results showed that dual-fitness function GA outperformed the classical single-fitness function GA with statistical significance for large problems and was competitive to IP, particularly when large size problems were used.

Keywords: Machine scheduling, Genetic algorithms, Due dates, Number of tardy jobs, Number of early jobs, Integer programming, Dual Fitness functions.

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