D. Whitley. A Genetic Algorithm Tutorial, Statistics and Computing (4):65-85, 1994. Home/Search Document Details and Download
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A New Learning Scheme for Neural Network Ensembles - Haraldsson, Ohlsson (2002) (Correct)
.... cancer [13] diagnosis of small round blue cell tumors using gene expression profiling [14] and detection of acute myocardial infarction using electrocardiograms [15] 16] Here we present an approach that shares some of its philosophy from evolutionary neural networks [8] and genetic algorithms [17]. The key idea is to obtain set of disagreeing network members by initially populating different parts of state space. The method creates a population that consists of many neural networks. The network population is trained using Monte Carlo techniques with a Boltzmann distribution type of ....
....so that the sampling is performed over the last two thirds of the post annealing phase. The final ensemble output hy k (p)i is calculated simply by averaging over all the network outputs in the ensemble. C. Similar Approaches The proposed method is to some extent similar to genetic algorithms [17]. Genetic algorithms evolve a population of models, measure their performance by an objective function f i and calculate the fitness as f i =hfi, whereupon a number of replicas are stochastically created in proportion to the fitness value. This approach is shared with the population method, for ....
D. Whitley, "A genetic algorithm tutorial," Statistics and Computing, vol. 4, pp. 65--85, 1994.
Robust Clustering by Evolutionary Computation - von der Gablentz, Köppen, al. (Correct)
....Genetic algorithms were introduced and investigated by John Holland [23] Later, they became popular by the book of Clustering Result (averagelinkage) Figure 2: Intertwined spirals clustered by the averagelinkage algorithm. David Goldberg [25] Also, consider the GA tutorial of David Whitley [26] as a very good introduction to the field. GAs and GPs are typically used for optimization problems. An optimization problem is given by a mapping F : X Y . The task is to find an element x 2 X for which y = f #x#; y 2Y is optimal in some sense. Genetic algorithms encodes a potential solution ....
D. Whitley. A genetic algorithm tutorial. In Statistics and Computing, 4, pages 65--85, 1994.
Genetic Algorithms in a Multi-Agent System - Vacher Jean-Philippe Galinho (1997) (Correct)
....on 10 bits, this manner, it is always possible to increase the knowledge to the level of our database. Figure 4. Binary representation of the structure of an agent Nevertheless, the size of our chromosome is important, in order to reduce the place in memory, we plan to use the coding of Gray [19]. Thus, we can use genetic algorithms on MAS. 1 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1 The Gray matrix that can be used to code genes. 1 0 0 0 1 1 0 0 1 1 1 0 1 1 1 1 The Gray matrix that can be used to decode genes. 3. The mutation function The mutation will correspond to the change of a bit, ....
D. Whitley, A Genetic Algorithm Tutorial, Colorado State University, 1993.
Approaches to Combining Local and Evolutionary Search for.. - Ku, Mak, Siu (Correct)
....nodes (five of them were dedicated as the output nodes) have been used to learn the long term dependency problem with a temporal length of five time steps. Therefore, there are a total of 12 Theta 12 12 Theta (3 1) 192 weights required to be optimized. Cellular genetic algorithms (GAs) [10,13,79], as described in Fig. 2, have been used to optimize the weights of RNNs. These weights are encoded as strings of floating point numbers. With a population size of 100 and a random walk of four steps, the cellular GA is able to find acceptable solutions for the long term dependency problem. The ....
D. Whitley. A genetic algorithm tutorial. Statistics & Computing, 4(2):65--85, 1994.
Genetic Algorithm for Traffic Sign Detection - Mainzer (2002) (Correct)
....algorithms required big computation performance, especially if a searched shape varies in dimension, stretch or spin (e.g. 2] 4] 5] The presented algorithm uses evolvable technique based on genetic algorithm for shape localisation. 2. Genetic algorithm overview Genetic algorithm (GA) e.g. 1][3]) is an adaptive method that mimics the metaphor of natural biological evolution. It is an optimisation technique that operates on population of individual solutions. Each individual solution (also called string or chromosom) represents a proposed solution of the solved problem. The theories of ....
Whitley D., A Genetic Algorithm Tutorial, Statistics and Computing 1994, Vol.4, pp. 65- 85
Towards a Game Agent - Niederberger, Gross (2002) (Correct)
....look at planning and acting which guides us to more complex AI systems and frameworks. 3.2 Adaptation and Learning This chapter will give the reader an overview on self adapting techniques for genetic evolution or learning. 3.2. 1 Genetic and Evolutionary Algorithms Genetic Algorithms (GA) 78][123][111] are used to solve a very complex problem with many degrees of freedom where normal computational power would not be sufficient to search the whole domain of possible states to get the very best solution. Instead this class of algorithms finds a good (but maybe not the best) solution. The ....
....reproduce. This leads towards local extrema in the domain of all possible states but the mutation or recombination process will help to overcome barriers and find other regions with even better solutions than the actual. See Tomassini for a good survey on GA [111] Whitley for a tutorial on GAs [123]. Mitchell and Forest [78] list the various forms of problems where GA have been successfully applied to: Optimization (numerical and combinatorical) automatic programming (evolving of computer programs, design of cellular automata and sorting networks) machine and robot learning ....
D. Whitley. "A genetic algorithm tutorial." Technical Report CS-93-103, Department of Computer Science, Colorado State University, 1993.
Hyperschema Theory for GP with One-Point Crossover, Building.. - Poli (2000) (Correct)
....by rooted trees or tree fragments. These definitions make schema theorem calculations easier. We describe these schema definitions below. This is a slightly different version of Holland s original theorem which applies when crossover is performed taking both parents from the mating pool [2, 22]. Rosca s Schemata q Fixed Size and Shape Schemata 2 = Sample X Programs Fig. 1. Examples of rooted schemata (top) and some instances of programs sampling them (bottom) Rosca s GP Schema Theory for Standard Crossover Rosca [5] has pro posed a definition of schema, called ....
D. Whitley, "A genetic algorithm tutorial," Tech. Rep. CS-93-103, Department of Computer Science, Colorado State University, August 1993.
Optimizing Hydrocarbon Field Development Using a Genetic Algorithm .. - Filho (1997) (1 citation) (Correct)
....1.0, the integer portion indicates how many copies of the string i are placed directly into the intermediate population. All strings (including those with i less than 1. 0) then place additional copies into the intermediate population with a probability corresponding to the fractional portion of i [Whitley, 1996]. Another more efficient way to implement the stochastic remainder selection is called Stochastic Universal Sampling [Baker, 1985] The population is randomly dis tributed over a pie where each individual has its slice proportional to its contribution to the total fitness. An outer roulette ....
.... the parents alldes but cannot have alldes which are not present in the parents chromosome strings ; Function evaluation: is defined as a measure of performance of a string representing a set of parameters and it is independent of the evaluation of any other string; Genitor GA (steady state class)[Whitley, 1996]: assumes that: a) reproduction produces one offspring at a time (b) the produced offspring replaces some rela tively less fit member of the population, so, the best points are maintained in the population (c) fitness is assigned according to rank rather than by fitness proportionate ....
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Whitley, D.: "A Genetic Algorithm Tutorial". Technical report, University of Colorado (1996).
Exact Schema Theory for GP and Variable-length GAs with.. - Poli, McPhee (2001) (1 citation) (Correct)
.... recent GA schema theorem for one point crossover [21, 22] and a refinement (in the absence of mutation) of both the GP schema theorem in [6] and Goldberg s version [19] of Holland s schema theory [18] The schema theorems in this paper also generalise other GA results (such as those summarised in [29]) as well as the result in [27, appendix] since they can be applied to linear schemata and even fixedlength binary strings. So, in the absence of mutation, the schema theory in this paper generalises and refines not only earlier GP schema theorems but also old and modern GA schema theories for ....
D. Whitley, "A genetic algorithm tutorial," Tech. Rep. CS93 -103, Department of Computer Science, Colorado State University, Aug. 1993.
A New Schema Theory for Genetic Programming with One-point.. - Poli, Langdon (1997) (5 citations) (Correct)
....two occurrences of ( x y) The tree fragment ( # x) is present in all programs in which an expression is added to x. This is a slightly different version of Holland s original theorem, which applies when crossover is performed taking both parents from the mating pool [Goldberg, 1989, Whitley, 1993] We use here the standard notation for multisets, which is slightly different from the one used in O Reilly s work. O Reilly s definition of schema allowed her to define the concept of order and defining length for GP schemata. In her definition the order of a schema is the number of non # ....
Whitley, D. (1993). A genetic algorithm tutorial. Technical Report CS-93-103, Department of Computer Science, Colorado State University.
Exact Schema Theory for GP and Variable-length GAs with.. - Poli, McPhee (2001) (1 citation) (Correct)
.... recent GA schema theorem for one point crossover [21, 22] and a refinement (in the absence of mutation) of both the GP schema theorem in [6] and Goldberg s version [19] of Holland s schema theory [18] The schema theorems in this paper also generalise other GA results (such as those summarised in [29]) as well as the result in [27, appendix] since they can be applied to linear schemata and even fixedlength binary strings. So, in the absence of mutation, the schema theory in this paper generalises and refines not only earlier GP schema theorems but also old and modern GA schema theories for ....
D. Whitley, "A genetic algorithm tutorial," Tech. Rep. CS93 -103, Department of Computer Science, Colorado State University, Aug. 1993.
Efficient Algorithms for Delay-bounded Minimum Cost Path.. - Girish Kumar Nishit (Correct)
....it does not find a near optimal (approximate) solution if there does not exist a delay bounded path between the source and the destination. We were therefore motivated to design a heuristic algorithm that overcomes these problems. Our heuristic is based on the paradigm of evolutionary computing [10]. A genetic algorithm (GA) can be used for solving combinatorial optimization problems of the type DBCP. GA derives inspiration from the process of natural evolution, where the properties of a species improve through generations. GA may be viewed as an iterative improvement heuristic with the ....
D. Whitley, "A genetic algorithm tutorial," Tech. Rep. CS-93-103, Colorado State University, 1993.
Search-Based Software Engineering - Harman, Jones (2001) (1 citation) (Correct)
....may need to shift, in order to recast it as a search based problem. For example, in the case of testing, the problem becomes one of searching for test cases which satisfy some test adequacy criterion. These issues are covered in far more detail in the general literature on metaheuristic search [17, 41, 43]. They are only mentioned here to demonstrate that the reformulation suggested and consequent development of Search Based Software Engineering, is conceptually feasible. 2 2.1 Representation The representation of a candidate solution is critical to shaping the nature of the search problem. ....
Whitley, D. A genetic algorithm tutorial. Statistics and Computing 4 (1994), 65-85.
A Genetic Algorithm Based Information Filter for Usenet - Alexios Chouchoulas Alexiosc (Correct)
.... Size One of the most obvious problems with NEWT s Genetic Algorithm is the relatively small number of evolving individuals (profiles) This is a problem for the standard reason: if the set of chromosomes is too small, the search is likely to never acquire specific schemata (parts of the solution) [22]. The result is equivalent to the degradation in genetic material observed in biology, when the gene pool is too limited. An improved Genetic Algorithm will need to increase the size of the population. This can mainly be done in two ways: ffl Add a constant number of profiles to the population ....
Darrell Whitley. A genetic algorithm tutorial. Technical Report CS-93-103, Colorado State University, 1993. ftp://ftp.cs.colostate.edu/pub/TechReports/1993/tr-103.ps.Z.
Exact Schema Theory for GP and Variable-length GAs with.. - Poli, McPhee (2001) (1 citation) (Correct)
....(Stephens and Waelbroeck 1997, Stephens and Waelbroeck 1999) and a refinement of both the GP schema theorem in (Poli and Langdon 1997) and Holland s work (Holland 1975) in the absence of mutation. The schema theorems in this paper also generalise other GA results (such as those summarised in (Whitley 1993)) as well as the result in (Altenberg 1995, appendix) since they can be applied to linear schemata and even fixed length binary strings. So, in the absence of mutation, the schema theory in this paper generalises and refines not only earlier GP schema theorems but also old and modern GA schema ....
Whitley, Darrell (1993). A genetic algorithm tutorial. Technical Report CS-93-103. Department of Computer Science, Colorado State University.
"Royal-Road" Landscapes for a Dual Genetic Algorithm - Collard, Gaspar (1996) (Correct)
....we discuss on the relevance of the Dual approach and its implications over the Genetic Algorithms perspectives of evolution. 1 Introduction This introduction features a brief overview of both traditional Genetic Algorithms (referred in the following as SGA) and Dual ones (DGA) Refer to [12] or [24] for more precisions on this field. 1.1 Simple Genetic Algorithms Genetic Algorithms use a kind of artificial Neo Darwinian evolution applied through successive populations (i.e. Generations) of individuals each coding in their(s) chromosome(s) a potential solution. The application of Genetic ....
D. Whitley, `A geneticalgorithm tutorial', TechnicalReport CS-93-103, University of Colorado, (1993). Genetic Algorithms and Neural Networks 217 P. Collard and A. Gaspar
Parallel Population Models for Genetic Algorithms - Schwehm (1996) (2 citations) (Correct)
....Local Model GREFENSTETTE (1981) Master Slave Network MANDERICK et al. 1989) R Algorithm Coarse Grain Fine Grain MACFARLANE et al. 1990) Farming Migration Diffusion GORGES SCHLEUTER (1992) Panmixia Migration Model Diffusion Model DORIGO et al. 1993) Island Model Neighbourhood M. WHITLEY (1993) Global Pop. Island Model Cellular GA CANT U PAZ (1995) Global Par. Coarse Grain Fine Grain Figure 1: Summary of classification schemes for parallel population models 3 Global Population Models Global models implement the classical genetic algorithm. As BETHKE (1976) has shown in a ....
WHITLEY, D. 1993. A Genetic Algorithm Tutorial. Technical Report CS-93-103. Dept. of Computer Science, Colorado State University.
Modifications of Genetic Algorithms for Designing and.. - Kinzel, Klawonn, Kruse (1994) (5 citations) (Correct)
....a bad rule base results in drastic changes for the fuzzy sets, it is better to divide these tasks. Binary versus Non Binary Codings The rule base as well as the fuzzy partitions are generally not given in a binary representation. Although binary codings are better suited for hyperplane sampling [18], for non binary codings more adequate mutation operators can be defined and the destroying effects of crossover are reduced. To see this consider for example a rule base for a fuzzy controller in the form of a table with 7 Theta 7 entries, where for each entry seven possible values ....
D. Whitley, A Genetic Algorithm Tutorial. Technical Report CS--93--103, Dept. of Computer Science, Colorado State University (1993).
Hyperschema Theory for GP with One-Point Crossover, Building.. - Poli (2000) (Correct)
....by rooted trees or tree fragments. These definitions make schema theorem calculations easier. We describe these schema definitions below. 1 This is a slightly different version of Holland s original theorem which applies when crossover is performed taking both parents from the mating pool [2, 22]. # 2 # x = 2 = Rosca s Schemata Fixed Size and Shape Schemata Sample Programs x 2 x x 2 x x 2 x x 2 2 x Fig. 1. Examples of rooted schemata (top) and some instances of programs sampling them (bottom) Rosca s GP Schema Theory for Standard ....
D. Whitley, "A genetic algorithm tutorial," Tech. Rep. CS-93-103, Department of Computer Science, Colorado State University, August 1993.
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D. Whitley. A Genetic Algorithm Tutorial, Statistics and Computing (4):65-85, 1994.
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....t (x i ) values and then selecting one of each N Gamma A sorted instances. For each instance selected, its value j t 1 (x i ) is increased by one. In our second version of AdaBoost.M1 the number of copies of x i to be present at C t 1 are assigned according to a Stochastic Universal Sampling [Whitley, 1993]. Stochastic Universal Sampling is a method for computing the number of copies assigned to an individual in a genetic algorithm. As in Adaboost. M1.1, for each instance x i , we calculate N w t (x i ) The quantity i = N w t (x i ) Gamma j t 1 (x i ) is kept for each instance. If N Gamma A ....
Whitley, L. D. (1993). A Genetic Algorithm Tutorial. Technical Report Nb. CS-93-103, Colorado State University.
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Whitley, D. (1994) A Genetic Algorithm Tutorial. To appear in Journal of Statistic and Computing. 11
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Whitley D (1994) A genetic algorithm tutorial. Statistics and Computing 4:65-85.
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Whitley, D. (1993). A genetic algorithm tutorial. Technical Report CS-93-103, Department of Computer Science, Colorado State University.
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D. Whitley. A genetic algorithm tutorial. Statistics and Computing, 4:65--85, 1994.
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Whitley, D.: A Genetic Algorithm Tutorial. Statistics and Computing 4 (1994) 65-85
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