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26
TestData Generation Using Genetic Algorithms
 Software Testing, Verification And Reliability
, 1999
"... This paper presents a technique that uses a genetic algorithm for automatic testdata generation. A genetic algorithm is a heuristic that mimics the evolution of natural species in searching for the optimal solution to a problem. In the testdata generation application, the solution sought by the ge ..."
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Cited by 130 (0 self)
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This paper presents a technique that uses a genetic algorithm for automatic testdata generation. A genetic algorithm is a heuristic that mimics the evolution of natural species in searching for the optimal solution to a problem. In the testdata generation application, the solution sought by the genetic algorithm is test data that causes execution of a given statement, branch, path, or definitionuse pair in the program under test. The testdatageneration technique was implemented in a tool called TGen in which parallel processing was used to improve the performance of the search. To experiment with TGen, a random testdata generator, called Random, was also implemented. Both TGen and Random were used to experiment with the generation of testdata for statement and branch coverage of six programs.
Escaping Hierarchical Traps with Competent Genetic Algorithms
 Proceedings of the Genetic and Evolutionary Computation Conference (GECCO2001
, 2001
"... To solve hierarchical problems, one must be able to learn the linkage, represent partial solutions efficiently, and assure effective niching. We propose the hierarchical ... ..."
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Cited by 87 (47 self)
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To solve hierarchical problems, one must be able to learn the linkage, represent partial solutions efficiently, and assure effective niching. We propose the hierarchical ...
Evaluationrelaxation schemes for genetic and evolutionary algorithms
, 2002
"... Genetic and evolutionary algorithms have been increasingly applied to solve complex, large scale search problems with mixed success. Competent genetic algorithms have been proposed to solve hard problems quickly, reliably and accurately. They have rendered problems that were difficult to solve by th ..."
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Cited by 61 (28 self)
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Genetic and evolutionary algorithms have been increasingly applied to solve complex, large scale search problems with mixed success. Competent genetic algorithms have been proposed to solve hard problems quickly, reliably and accurately. They have rendered problems that were difficult to solve by the earlier GAs to be solvable, requiring only a subquadratic number of function evaluations. To facilitate solving largescale complex problems, and to further enhance the performance of competent GAs, various efficiencyenhancement techniques have been developed. This study investigates one such class of efficiencyenhancement technique called evaluation relaxation. Evaluationrelaxation schemes replace a highcost, lowerror fitness function with a lowcost, higherror fitness function. The error in fitness functions comes in two flavors: Bias and variance. The presence of bias and variance in fitness functions is considered in isolation and strategies for increasing efficiency in both cases are developed. Specifically, approaches for choosing between two fitness functions with either differing variance or differing bias values have been developed. This thesis also investigates fitness inheritance as an evaluation
Expanding From Discrete To Continuous Estimation Of Distribution Algorithms: The IDEA
 In Parallel Problem Solving From Nature  PPSN VI
, 2000
"... . The direct application of statistics to stochastic optimization based on iterated density estimation has become more important and present in evolutionary computation over the last few years. The estimation of densities over selected samples and the sampling from the resulting distributions, i ..."
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Cited by 32 (8 self)
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. The direct application of statistics to stochastic optimization based on iterated density estimation has become more important and present in evolutionary computation over the last few years. The estimation of densities over selected samples and the sampling from the resulting distributions, is a combination of the recombination and mutation steps used in evolutionary algorithms. We introduce the framework named IDEA to formalize this notion. By combining continuous probability theory with techniques from existing algorithms, this framework allows us to dene new continuous evolutionary optimization algorithms. 1 Introduction Algorithms in evolutionary optimization guide their search through statistics based on a vector of samples, often called a population. By using this stochastic information, non{deterministic induction is performed in order to attempt to use the structure of the search space and thereby aid the search for the optimal solution. In order to perform induct...
Analysis and Improvement of Fitness Exploitation in XCS: Bounding Models, Tournament Selection, and Bilateral Accuracy
 EVOLUTIONARY COMPUTATION
, 2003
"... The evolutionary learning mechanism in XCS strongly depends on its accuracybased fitness approach. The approach is meant to result in an evolutionary drive from classifiers of low accuracy to those of high accuracy. Since, given inaccuracy, lower specificity often corresponds to lower accuracy fitn ..."
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Cited by 28 (17 self)
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The evolutionary learning mechanism in XCS strongly depends on its accuracybased fitness approach. The approach is meant to result in an evolutionary drive from classifiers of low accuracy to those of high accuracy. Since, given inaccuracy, lower specificity often corresponds to lower accuracy fitness pressure most often also results in a pressure towards higher specificity. Moreover
On The Supply Of Building Blocks
 Proceedings of the Genetic and Evolutionary Computation Conference
, 2001
"... This study addresses the issue of buildingblock supply in the initial population. ..."
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Cited by 27 (14 self)
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This study addresses the issue of buildingblock supply in the initial population.
Immunology as Information Processing
 Design Principles for the Immune System and Other Distributed Autonomous Systems
, 2000
"... This chapter describes the behavior of the immune system from an informationprocessing perspective. It reviews a series of projects conducted at the University of New Mexico and the Santa Fe Institute, which have developed and explored the theme "immunology as information processing." The ..."
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Cited by 26 (0 self)
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This chapter describes the behavior of the immune system from an informationprocessing perspective. It reviews a series of projects conducted at the University of New Mexico and the Santa Fe Institute, which have developed and explored the theme "immunology as information processing." The projects cover the spectrum from serious modeling of real immunological phenomena, such as crossreactive responses in animals and the generation of diversity, to computer science applications, especially the attempt to develop an immune system for computers to protect them against viruses, intrusions, and other malicious activities. In each project, we have used an approach with the following steps: (1) Identify a specific mechanism that appears to be interesting computationally, (2) write a computer program that implements or models the mechanism, (3) study its properties through simulation and mathematical analysis, and (4) demonstrate its capabilities, either by applying the ...
An Algorithmic Framework For Density Estimation Based Evolutionary Algorithms
, 1999
"... The direct application of statistics to stochastic optimization in evolutionary computation has become more important and present over the last few years. With the introduction of the notion of the Estimation of Distribution Algorithm (EDA), a new line of research has been named. The application are ..."
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Cited by 24 (5 self)
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The direct application of statistics to stochastic optimization in evolutionary computation has become more important and present over the last few years. With the introduction of the notion of the Estimation of Distribution Algorithm (EDA), a new line of research has been named. The application area so far has mostly been the same as for the classic genetic algorithms, being the binary vector encoded problems. The most important aspect in the new algorithms is the part where probability densities are estimated. In probability theory, a distinction is made between discrete and continuous distributions and methods. Using the rationale for density estimation based evolutionary algorithms, we present an algorithmic framework for them, named IDEA. This allows us to define such algorithms for vectors of both continuous and discrete random variables, combining techniques from existing EDAs as well as density estimation theory. The emphasis is on techniques for vectors of continuous random variables, for which we present new algorithms in the field of density estimation based evolutionary algorithms, using two different density estimation models.
Applying SelfOrganised Criticality to Evolutionary Algorithms
"... Complex systems are typically composed of a large number of locally interacting components that operate at a critical state between chaos and order, which is known as selforganised criticality. A common feature of this state is the exponential (power law) relationship between the frequency of an ev ..."
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Cited by 15 (8 self)
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Complex systems are typically composed of a large number of locally interacting components that operate at a critical state between chaos and order, which is known as selforganised criticality. A common feature of this state is the exponential (power law) relationship between the frequency of an event and the size of its impact, such as the event of an earthquake and its strength on the Richter scale. Most state transitions in a component of a complex system only affect its neighbourhood, but once in a while entire avalanches of propagating state transitions can lead to a major recon guration of the system. In evolution, this system behaviour has been identified in species extinction on an evolutionary timescale, where avalanches correspond to mass extinction. In this paper, we applied the concept of selforganised criticality (SOC) to control mutation on the individual level and extinction on the population level in the context of evolutionary algorithms (EA). Our resul...
Modeling Tournament Selection With Replacement Using Apparent Added Noise
 Intelligent Engineering Systems Through Artificial Neural Networks, 11 , 129–134. (Also IlliGAL
, 2001
"... This paper analyzes the effects of tournament selection (Goldberg, Korb, & Deb, 1989) with replacement (TWR) on the convergence time and population sizing for selectorecombinative genetic algorithms. In contrast to tournament selection without replacement (TWOR), TWR has not received considerabl ..."
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Cited by 15 (6 self)
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This paper analyzes the effects of tournament selection (Goldberg, Korb, & Deb, 1989) with replacement (TWR) on the convergence time and population sizing for selectorecombinative genetic algorithms. In contrast to tournament selection without replacement (TWOR), TWR has not received considerable analytical attention in genetic algorithms literature. TWR is usually considered to be equivalent to TWOR. However, we empirically show that TWR requires more function evaluations for attaining the same accuracy as TWOR