This paper presents a technique that uses a genetic algorithm for automatic test-data 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 test-data generation application, the solution sought by the genetic algorithm is test data that causes execution of a given statement, branch, path, or definition-use pair in the program under test. The test-data-generation 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 test-data generator, called Random, was also implemented. Both TGen and Random were used to experiment with the generation of test-data for statement and branch coverage of six programs.

To solve hierarchical problems, one must be able to learn the linkage, represent partial solutions efficiently, and assure effective niching. We propose the hierarchical ...

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 large-scale complex problems, and to further enhance the performance of competent GAs, various efficiency-enhancement techniques have been developed. This study investigates one such class of efficiency-enhancement technique called evaluation relaxation. Evaluation-relaxation schemes replace a high-cost, low-error fitness function with a low-cost, high-error 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-

This study addresses the issue of buildingblock supply in the initial population.

The evolutionary learning mechanism in XCS strongly depends on its accuracy-based 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

. 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...

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 ...

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.

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

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 self-organised 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 time-scale, where avalanches correspond to mass extinction. In this paper, we applied the concept of self-organised criticality (SOC) to control mutation on the individual level and extinction on the population level in the context of evolutionary algorithms (EA). Our resul...