J. L. R. Filho, P. C. Treleaven, and C. Alippi, "Genetic Algorithm Programming Environments", IEEE Computer (June 1994), pp. 29--42.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....are applied on these strings to yield a new generation of strings. The process of selection, crossover and mutation continues for a xed number of generations or till a termination condition is satis ed. An excellent survey of GAs along with the programming structure used can be found in Ref. [4]. GAs have applications in elds as diverse as VLSI design, image processing, neural networks, machine learning, jobshop scheduling, etc. 5 10] In the area of pattern recognition, there are many tasks involved in the process of analyzing identifying a pattern which need appropriate parameter ....

J.L.R. Filho, P.C. Treleaven, C. Alippi, Genetic algorithm programming environments, IEEE Comput. 27 (1994) 28}43.

....class boundaries. Results are compared extensively with those of the Bayes classifier, k NN rule and multilayer perceptron. Index Terms Evolutionary computation, hyperplane fitting, pattern recognition, variable mutation probability. I. INTRODUCTION G ENETIC algorithms (GA s) 1] [5] are randomized search and optimization techniques guided by the principles of evolution and natural genetics, and have a large amount of implicit parallelism. GA s perform multimodal search in complex landscapes and provide near optimal solutions for objective or fitness function of an ....

....are applied on these strings to yield a new generation of strings. The process of selection, crossover and mutation continues for a fixed number of generations or until a termination condition is satisfied. An excellent survey of GA s along with the programming structure used can be found in [5]. In this paper, an attempt is made to study the application of GA s for pattern classification in N dimensional data space. Classification is a problem of generating decision boundaries Manuscript received April 26, 1995; revised March 17, 1996 and July 5, 1997. This work was carried out when S. ....

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J. L. R. Filho and P. C. Treleavan, "Genetic algorithm programming environments," IEEE Comput., pp. 28--43, June 1994.

....list. To achieve a low time complexity, the proposed algorithm is parallelized. The algorithm scales well with the number of processors. Moreover, the algorithm can handle general DAGs without making simplifying assumptions. Inspired by the Darwinian concept of evolution, genetic algorithms [45] [56], 73] 87] 176] are global search techniques which explore different regions of the search space simultaneously by keeping track of a set of potential solutions called a population. According to the Building block Hypothesis [73] and the Schema Theorem [73] a genetic algorithm systematically ....

....genetic algorithms (SGA) followed by a discussion of different models of parallel genetic algorithms (PGA) 4.2. 1 Standard Genetic Algorithms Genetic algorithms (GAs) introduced by Holland in the 1970 s [87] are search techniques that are designed based on the concept of evolution [17] 45] [56], 73] 176] In simple terms, given a well defined search space in which each point is represented by a bit string called a chromosome, a GA is applied with its three genetic search operators selection, crossover, and mutation with the objective of improving the quality of the chromosomes. A ....

J.L.R. Filho, P.C. Treleaven, and C. Alippi, "Genetic-Algorithm Programming Environments," IEEE Computer, Jun. 1994, pp. 28-43.

....Ahuactzin, Juan Manuel, 984, 985, 988, 989, 990, 992] Ait Boudaoud, D. 171, 172] Aizawa, Akiko N. 50] Akagi, Shinsuke, 322] Akiyama, Mamory, 51] Alander, Jarmo T. 27, 28, 29, 30, 22, 23] Alba Torres, Enrique A. 52, 53, 54] Aldana Montes, Jos e Francisco, 52, 54] Alippi, Cesare, [55] Allen, Franklin, 550] Allen, L. 152] Alliot, Jean Marc, 56] Altman, Erik R. 1026] Amari, Sun ichi, 121] Anderson, C. W. 1065] Anderson, Peter G. 779] Angeline, Peter J. 57, 817, 818] Anheyer, Thomas, 1035] Annicchiarico, W. 366] Anon. 34, 66, 260, 489, 509, 776, 778] ....

....Fanelli, A. M. 8] Fang, Hsiao Lan, 270, 785] Fang, J. H. 557] Farrell, Chris, 271] Fekadu, Adhanom A. 465] Feldman, David S. 272] Feng, S. 607] Ferland, Jacques A. 273] Ferri, F. 274] Ficek, Rhonda Janes, 275] Fickett, J. W. 276] File, P. E. 1053] Filho, J. R. [55, 277, 278] Filipic, Bogdan, 279, 280, 281] Fleming, Peter J. 282, 283, 284, 285, 286, 287, 288] Fleurent, Charles, 273] Flockton, Stuart J. 662, 663, 664] Floreano, Dario, 289] Fogarty, Terence C. 290, 291, 292, 293, 294] Fogel, David B. 214, 295, 296, 3, 297, 298, 299, 300, 301, 302, 303, ....

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J. R. Filho, Cesare Alippi, and P. Treleaven. Genetic algorithm programming environments. In Stender

....quality, or to lower the timecomplexity, or both. In the following, we briefly outline some of these recent advancements. At the end of this section, we also indicate some current research trends in scheduling. 70 8. 1 Scheduling using Genetic Algorithms Genetic algorithms (GAs) 42] [56], 58] 68] 78] 157] have recently found many applications in optimization problems including scheduling [11] 19] 27] 44] 80] 147] GAs use global search techniques to explore different regions of the search space simultaneously by keeping track of a set of potential solutions of ....

J.L.R. Filho, P.C. Treleaven, and C. Alippi, "Genetic-Algorithm Programming Environments," IEEE Computer, June 1994, pp. 28-43.

.... with easy, yet powerful way to construct and customize a multimedia user interface by using Java together with a scripting language JavaScript [23] and various plug ins such as Cosmo Player for VRML (Virtual Reality Modeling Language) In recent years, a number of tools for GA have been developed [8] primarily for the following purposes. 1 ffl To support the design and or implementation of a GA for a particular problem ffl To conduct simulation of various GAs and investigate properties of and new ideas for GAs empirically Although there has been great progress in theory of GA, it is not ....

....yet to give us principles of GA design, e.g. which GA operators should be used and what parameter values are best. Thus, the design of a particular genetic algorithm used in practice needs extensive experiments on its performance by simulation. With a few exceptions such as PGAPack and GAME [8], most of general purpose tools cannot be used for distributed genetic algorithms (DGAs) In addition, many of the existing tools are implemented in C and only some in object oriented languages such as C . Thus, empirical study on DGA has usually been conducted by implementing ad hoc simulation ....

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Filho, J. L. and Treleaven, P. C. Genetic-algorithm programming environments. Computer, 27, 6 (June 1994), pp. 28--43.

....are preferred. On the other hand, papers should also focus on analysis and show differences from more traditional heuristics in an easily understandable way (see e.g. MH93] discuss possible alternative directions of research [Khu94, KBH94a, KBH94b] give gentle surveys on a particular issue [FAT94], or provide tutorial introductions to a whole EC paradigm [Whi93, BBM93a, BBM93b] Also, apart from these sometimes rather dry theoretical approaches to the research field, almost all currently available software packages which are related to the EC field have been included into SAFIER for the ....

J.R. Filho, C. Alippi, and P. Treleaven. Genetic algorithm programming environments. IEEE Computer, page (to appear), February 1994. Available via anonymous FTP as: sfi.santafe.edu:/pub/EC/GA/papers/ieee94.ps.

....for the floating point vector representation. A GA uses a set of randomized genetic operators to evolve a finite population of finite code strings over a series of generations (Holland 1975; Goldberg 1989; Grefenstette and Baker 1989) The operators used in GA implementations vary (Davis 1991; Filho, Treleaven and Alippi 1994), but in an analytical sense the basic set of operators can be defined as reproduction, mutation, crossover and inversion. The wide variety of implementations of these operators reflects the variety of codings that are best suited for different applications. Reproduction entails selecting a ....

Filho, Jose L. Ribeiro, Philip C. Treleaven and Cesare Alippi. 1994. "Genetic Algorithm Programming Environments." Computer 27:28--43.

....is robustness against ill natured property of the target problems. Indeed, genetic algorithms can be robust enough for noisy situation, since they do not use any gradient information of design space. They also have some ability for searching globally optimal solutions against multi modality. Filho et al. 1994) categorized genetic algorithms into guided random search techniques as well as simulated annealing. This intention well corresponds to how to save computation times of design evaluation when some kinds of random search mechanisms are significantly necessary. As for multi objective optimization, ....

Filho, J. L. R., Treleaven, P. C. and Alippi, C., 1994, "GeneticAlgorithm Programming Environments," IEEE Computer, Vol. 27, No. 6, pp. 28-43.

.... modelling and decision making, as well as in so called bio informatics [4] engineering and industry [5] As parallelism is an intrinsic feature of GAs, many implementation of parallel genetic algorithms are reported [6, 7] After analysis of publicly available libraries and systems of GAs [8, 9] we have decided to develop parallel genetic algorithms library which should enable easy creation of parallel programs exploiting GA approach. The library should be flexible, easy maintainable, with maximal attainable performance of its routines and portable to wide range parallel computing ....

J.R. Filho, C. Alippi, P. Treleaven, "Genetic Algorithm Programming Environments ", Department of Computer Science, University College, London, 1994. (ENCORE: .../EC/GA/papers/ieee94.ps.gz)

....the evolution of a set of decision rules (programs) for classifying those examples. Once found the rule base (knowledge base) can be used to classify new examples. BEAGLE is commercially available and has been widely applied; eg in insurance, weather forecasting, finance and forensic science [JRFT94] Handley [Han93] uses genetic programming to predict the shape of proteins. He was able to evolve programs which, using the protein s chemical composition, were able to predict whether each part of a protein would have a particular geometric shape (an ff helix) or not. Genetic programming was ....

C. Alippi J. R. Filho and P. Treleaven. Genetic algorithm programming environments. IEEE Computer Journal, Jun 1994.

....and to select one that satisfies constraints. Fifth, we need partitioning algorithms to efficiently explore a subset of the huge number of possible partitions. Commonly used classes of algorithms include clustering algorithms [30] iterative improvement algorithms [31, 32] genetic algorithms [33], and custom algorithms [26, 34] Some algorithms are fast, such as clustering, while other algorithms are slower but often find better solutions, like genetic algorithms. A variety of techniques have evolved to assist the designer perform functional partitioning. We can form three categories of ....

J. Filho and P. Treleaven, "Genetic-algorithm programming environments," IEEE Computer, vol. 27, pp. 28--43, June 1994.

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J. L. R. Filho, P. C. Treleaven, and C. Alippi, "Genetic Algorithm Programming Environments", IEEE Computer (June 1994), pp. 29--42.

No context found.

Filho, J. L. R., Treleaven, P. C., & Alippi, C. (1994). Genetic-algorithm programming environments. Computer, 27(6), 28--43.

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J. Ribeiro Filho, P. Treleaven, and C. Alippi, "Genetic-algorithm programming environments," Computer, vol. 27, no. 6, pp. 28-43, 1994.

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