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Journal ArticleDOI

Performance evaluation of genetic algorithms and evolutionary programming in optimization and machine learning

01 Apr 2002-Cybernetics and Systems (Informa UK Ltd)-Vol. 33, Iss: 3, pp 203-223
TL;DR: Genetic Algorithms and Evolutionary Programming are investigated here in both optimization and machine learning, showing that while both algorithms may look similar in many ways their performance may differ for some applications.
Abstract: Genetic Algorithms (GAs) and Evolutionary Programming (EP) are investigated here in both optimization and machine learning. Adaptive and standard versions of the two algorithms are used to solve novel applications in search and rule extraction. Simulations and analysis show that while both algorithms may look similar in many ways their performance may differ for some applications. Mathematical modeling helps in gaining better understanding for GA and EP applications. Proper tuning and loading is a key for acceptable results. The ability to instantly adapt within an unpredictable and unstable search or learning environment is the most important feature of evolution-based techniques such as GAs and EP.
Citations
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Journal ArticleDOI
TL;DR: The main highlights of this paper are threefold, i.e., the operation-based sequence model, the problem-dependent job assignment rules and the novel evolutionary framework of ENSHA, which adopts the elitist nondominated sorting method for evolving MP to maintain high-quality solutions regarding both the convergence and diversity.
Abstract: In this paper, an elitist nondominated sorting hybrid algorithm, namely ENSHA, is proposed to solve the multi-objective flexible job-shop scheduling problem (MOFJSSP) with sequence-dependent setup times/costs (MOFJSSP_SDST/C). The objectives to be minimized are the maximal completion time (i.e., makespan) and the total setup costs (TSC). The makespan is an efficiency-focused objective whereas the TSC is an economic focused one. Existing works mainly consider the efficiency-focused multiple criteria. The main highlights of this paper are threefold, i.e., the operation-based sequence model, the problem-dependent job assignment rules and the novel evolutionary framework of ENSHA. For the operation-based sequence model, this is the first time that the sequence model of MOFJSSPs has been proposed and the TSC has been treated as an independent objective in MOFJSSPs. For the job assignment rules, the solution representation is first proposed, and then three job assignment rules are specifically designed to decode solutions or sequences into feasible scheduling schemes. For the novel evolutionary framework, it works with two populations, i.e., the main population (MP) and the auxiliary population (AP). First, ENSHA adopts the elitist nondominated sorting method for evolving MP to maintain high-quality solutions regarding both the convergence and diversity. Next, a machine learning strategy based on the estimation of distribution algorithm (EDA) is proposed to learn the valuable information from nondominated solutions in MP for building a probabilistic model. This model is then used to generate the offspring of AP. Furthermore, a simple yet effective cooperation-based refinement mechanism is raised to combine MP and AP, so as to generate MP of the next generation. Finally, experimental results on 39 benchmark instances and a real-life case study demonstrate the effectiveness and application values of the proposed ENSHA.

46 citations

01 Jan 2008

12 citations


Cites background from "Performance evaluation of genetic a..."

  • ...implicit parallelism, [315] in machine learning, [422, 135] in training neural networks, [488] in TSP, [222, 287]...

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  • ..., [1845] Current Opinion in Biotechnology, [2123] Current Science, [1820] Cybernetics and Systems, [135, 243, 287, 726, 2441, 1961] Daziran Tansuo, [543] DDT, [1847] Decis Support Syst....

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  • ..., [135]...

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Journal Article
TL;DR: A hybrid technique for the recognition of typed Arabic characters that is compact, online, robust, and feasible from hardware point of view and uses Learning Vector Quantization (LVQ) technique for classifying the same chain.
Abstract: This article presents a hybrid technique for the recognition of typed Arabic characters Due to its curved and continuous nature, Arabic text has to go through words segmentation, character segmentation, feature extraction, and finally character recognition In this work, Freeman Chain (FC) technique [20, 21] is used to generate a chain for every segmented character This chain represents the extracted features Moreover, two approaches are presented for the classification process In the first approach, we use a classical sequential weighing algorithm that finds the closest available “Standard Character Template” to the extracted chain In the second approach, we use Learning Vector Quantization (LVQ) (specifically LVQ3) technique for classifying the same chain To improve the performance of that LVQ, the Genetic Algorithm (GA) [11, 23] is invoked for some additional training We call our neural network with the GA “GALVQ3” For further robustness testing of both approaches, we add some artificial noise to the extracted chains and repeat simulations In general, LVQ techniques provide higher classification rate even for cases where noise and partial observations exist As a result, the GALVQ3 classifier is compact, online, robust, and feasible from hardware point of view

2 citations


Cites methods from "Performance evaluation of genetic a..."

  • ...Previous techniques, such as horizontal and vertical projections have weakness in extracting some features of Arabic characters [1, 2]....

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Journal ArticleDOI
TL;DR: In this paper , a review of techniques used for the detection and tracking of UAVs or drones is presented, which are different techniques that depend on collecting measurements of the position, velocity, and image of the UAV and then using them in detecting and tracking.
Abstract: This paper presents a review of techniques used for the detection and tracking of UAVs or drones. There are different techniques that depend on collecting measurements of the position, velocity, and image of the UAV and then using them in detection and tracking. Hybrid detection techniques are also presented. The paper is a quick reference for a wide spectrum of methods that are used in the drone detection process.
References
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Book
01 Sep 1988
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Abstract: From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required

52,797 citations

01 Jan 1989
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Abstract: From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields. Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required.

33,034 citations


"Performance evaluation of genetic a..." refers background or methods in this paper

  • ...1986), in addition to a lot of literature investigating theoretical analysis and modeling (Holland 1986; Goldberg 1989)....

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  • ...His student, David Goldberg (Goldberg 1989; Booker, Goldberg, and Holland 1989) was one of the major contributors to the publicity of the GA among AI community....

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  • ...One of the machine learning paradigms that uses GA is the classi®er systems (Goldberg 1989)....

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  • ...Many applications in global optimization has been solved with GA and EP (Fogel 1991; Rumelhart et al. 1986), in addition to a lot of literature investigating theoretical analysis and modeling (Holland 1986; Goldberg 1989)....

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Book
01 Jan 1975
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Abstract: Name of founding work in the area. Adaptation is key to survival and evolution. Evolution implicitly optimizes organisims. AI wants to mimic biological optimization { Survival of the ttest { Exploration and exploitation { Niche nding { Robust across changing environments (Mammals v. Dinos) { Self-regulation,-repair and-reproduction 2 Artiicial Inteligence Some deenitions { "Making computers do what they do in the movies" { "Making computers do what humans (currently) do best" { "Giving computers common sense; letting them make simple deci-sions" (do as I want, not what I say) { "Anything too new to be pidgeonholed" Adaptation and modiication is root of intelligence Some (Non-GA) branches of AI: { Expert Systems (Rule based deduction)

32,573 citations


"Performance evaluation of genetic a..." refers background in this paper

  • ...The GA in its known form, was introduced by Holland (1975)....

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Journal ArticleDOI
TL;DR: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving and a number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class.
Abstract: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class. These theorems result in a geometric interpretation of what it means for an algorithm to be well suited to an optimization problem. Applications of the NFL theorems to information-theoretic aspects of optimization and benchmark measures of performance are also presented. Other issues addressed include time-varying optimization problems and a priori "head-to-head" minimax distinctions between optimization algorithms, distinctions that result despite the NFL theorems' enforcing of a type of uniformity over all algorithms.

10,771 citations