Showing papers on "Quality control and genetic algorithms published in 1993"

Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization

Abstract: The paper describes a rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified to allow direct intervention of an external decision maker (DM). Finally, the MOGA is generalised further: the genetic algorithm is seen as the optimizing element of a multiobjective optimization loop, which also comprises the DM. It is the interaction between the two that leads to the determination of a satisfactory solution to the problem. Illustrative results of how the DM can interact with the genetic algorithm are presented. They also show the ability of the MOGA to uniformly sample regions of the trade-off surface.

An overview of evolutionary algorithms for parameter optimization

Abstract: Three main streams of evolutionary algorithms (EAs), probabilistic optimization algorithms based on the model of natural evolution, are compared in this article: evolution strategies (ESs), evolutionary programming (EP), and genetic algorithms (GAs). The comparison is performed with respect to certain characteristic components of EAs: the representation scheme of object variables, mutation, recombination, and the selection operator. Furthermore, each algorithm is formulated in a high-level notation as an instance of the general, unifying basic algorithm, and the fundamental theoretical results on the algorithms are presented. Finally, after presenting experimental results for three test functions representing a unimodal and a multimodal case as well as a step function with discontinuities, similarities and differences of the algorithms are elaborated, and some hints to open research questions are sketched.

Genetic algorithms: principles of natural selection applied to computation

Abstract: A genetic algorithm is a form of evolution that occurs on a computer. Genetic algorithms are a search method that can be used for both solving problems and modeling evolutionary systems. With various mapping techniques and an appropriate measure of fitness, a genetic algorithm can be tailored to evolve a solution for many types of problems, including optimization of a function of determination of the proper order of a sequence. Mathematical analysis has begun to explain how genetic algorithms work and how best to use them. Recently, genetic algorithms have been used to model several natural evolutionary systems, including immune systems.

A note on genetic algorithms for large-scale feature selection

Abstract: We introduce the use of genetic algorithms (GA) for the selection of features in the design of automatic pattern classifiers. Our preliminary results suggest that GA is a powerful means of reducing the time for finding near-optimal subsets of features from large sets.

Serial and Parallel Genetic Algorithms as Function Optimizers

Abstract: {PG,1 A standard capitulum type of chrysanthemum plant which is a sport of the non-commercial cultivar Arcade, disclosed in U.S. Plant Pat. No. 3,659, granted Nov. 26, 1974; Brocade being distinguished from the parent cultivar by its medium to dark yellow ray floret color, taller plant height, larger flower size, and longer flowering response period.

Using genetic algorithms to explore pattern recognition in the immune system

Abstract: This paper describes an immune system model based on binary strings. The purpose of the model is to study the pattern-recognition processes and learning that take place at both the individual and species levels in the immune system. The genetic algorithm (GA) is a central component of the model. The paper reports simulation experiments on two pattern-recognition problems that are relevant to natural immune systems. Finally, it reviews the relation between the model and explicit fitness-sharing techniques for genetic algorithms, showing that the immune system model implements a form of implicit fitness sharing.

Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques

Abstract: This paper proposes using fuzzy logic techniques to dynamically control parameter settings of genetic algorithms (GAs). We describe the Dynamic Parametric GA: a GA that uses a fuzzy knowledge-based system to control GA parameters. We then introduce a technique for automatically designing and tuning the fuzzy knowledge-base system using GAs. Results from initial experiments show a performance improvement over a simple static GA. One Dynamic Parametric GA system designed by our automatic method demonstrated improvement on an application not included in the design phase, which may indicate the general applicability of the Dynamic Parametric GA to a wide range of applications.

Cellular Genetic Algorithms

Abstract: In this chapter, we introduce the applications of cellular automata in genetic algorithms. In the traditional sense, genetic algorithms (GA) originated from Darwin’s evolution theory. Borrowing from the natural law of “survival of the fittest”, through the genetic operations of selection, crossover and mutation, the individual’s adaptability gets improved. One important feature of genetic algorithms is that the optimization process is not dependent on gradient information, which makes it especially suitable for dealing with complex and nonlinear problems which are difficult to be solved by general searching methods.

Integrated Genetic Algorithm for Optimization of Space Structures

Abstract: Gradient‐based mathematical‐optimization algorithms usually seek a solution in the neighborhood of the starting point. If more than one local optimum exists, the solution will depend on the choice of the starting point, and the global optimum cannot be found. This paper presents the optimization of space structures by integrating a genetic algorithm with the penalty‐function method. Genetic algorithms are inspired by the basic mechanism of natural evolution, and are efficient for global‐searches. The technique employs the Darwinian survival‐of‐the‐fittest theory to yield the best or better characters among the old population, and performs a random information exchange to create superior offspring. Different types of crossover operations are used in this paper, and their relative merit is investigated. The integrated genetic algorithm has been implemented in C language and is applied to optimization of three space truss structures. In each case, an optimum solution was obtained after a limited number of it...

Case-Based Initialization of Genetic Algorithms

Abstract: In this paper, we introduce a case-based method of initializing genetic algorithms that are used to guide search in changing environments. This is incorporated in an anytime learning system. Anytime learning is a general approach to continuous learning in a changing environment. The agent’s learning module continuously tests new strategies against a simulation model of the task environment, and dynamically updates the knowledge base used by the agent on the basis of the results. The execution module includes a monitor that can dynamically modify the simulation model based on its observations of the external environment; an update to the simulation model causes the learning system to restart learning. Previous work has shown that genetic algorithms provide an appropriate search mechanism for anytime learning. This paper extends the approach by including strategies, which are learned under similar environmental conditions, in the initial population of the genetic algorithm. Experiments show that case-based initialization of the population results in a significantly improved performance.

Genetic Reinforcement Learning for Neurocontrol Problems

Abstract: Empirical tests indicate that at least one class of genetic algorithms yields good performance for neural network weight optimization in terms of learning rates and scalability. The successful application of these genetic algorithms to supervised learning problems sets the stage for the use of genetic algorithms in reinforcement learning problems. On a simulated inverted-pendulum control problem, “genetic reinforcement learning” produces competitive results with AHC, another well-known reinforcement learning paradigm for neural networks that employs the temporal difference method. These algorithms are compared in terms of learning rates, performance-based generalization, and control behavior over time.

Robust feature selection algorithms

Abstract: Selecting a set of features which is optimal for a given task is a problem which plays an important role in wide variety of contexts including pattern recognition, adaptive control and machine learning. Experience with traditional feature selection algorithms in the domain of machine learning leads to an appreciation for their computational efficiency and a concern for their brittleness. The authors describe an alternative approach to feature selection which uses genetic algorithms as the primary search component. Results are presented which suggested that genetic algorithms can be used to increase the robustness of feature selection algorithms without a significant decrease in compuational efficiency.

An evaluation of local improvement operators for genetic algorithms

Abstract: Genetic algorithms have demonstrated considerable success in providing good solutions to many NP-hard optimization problems. For such problems, exact algorithms that always find an optimal solution are only useful for small toy problems, so heuristic algorithms such as the genetic algorithm must be used in practice. In this paper, we apply the genetic algorithm to the NP-hard problem of multiple fault diagnosis (MFD). We compare a pure genetic algorithm with several variants that include local improvement operators. These operators, which are often domain-specific, are used to accelerate the genetic algorithm in converging on optimal solutions. Our empirical results indicate that by using the appropriate local improvement operator, the genetic algorithm is able to find an optimal solution in all but a tiny fraction of the cases and at a speed orders of magnitude faster than exact algorithms. >

Evolving a sort: lessons in genetic programming

Abstract: In applying the genetic programming paradigm to the task of evolving iterative sorting algorithms, a variety of lessons are learned. With proper selection of the primitives, sorting algorithms are evolved that are both general and non-trivial. The sorting problem is used as a testbed to evaluate the value of several alternative parameters, with some small gains shown. The value of applying steady state genetic algorithm techniques to genetic programming, called steady state genetic programming, is demonstrated. One unusual genetic operator is created, i.e., nonfitness single cross-over. It shows promise in at least this environment. >

Genetic algorithms

Abstract: Genetic algorithms are exploratory procedures that are often able to locate near optimal solutions to complex problems. To do this, a genetic algorithm maintains a set of trial solutions, and forces them to evolve towards an acceptable solution. First, a representation for possible solutions must be developed. Then, starting with an initial random population and employing survival-of-the-fittest and exploiting old knowledge in the gene pool, each generation's ability to solve the problem should improve. This is achieved through a four-step process involving evaluation, reproduction, recombination, and mutation. As an application the author developed a genetic algorithm to train a product neural network for predicting the optimum transistor width in a CMOS switch, given the operating conditions and desired conductance. >

Genetic algorithms as a computational tool for design

Abstract: Design is an ubiquitous activity embracing most of engineering and architecture. Because design is so pervasive, any research that leads to improvements in design processes or products can have great impact. Current efforts at capturing the design process in a computational framework do not pay heed to the evolutionary aspect of prototype creation and ongoing refinement. Further, in poorly-understood domains where expert knowledge or previous experience is lacking, current systems do not perform well. Genetic algorithms are stochastic parallel search algorithms that model natural selection, the process of evolution. Over time natural selection has produced a wide range of robust structures (life forms) that efficiently perform a broad range of functions. The success of natural selection on earth provides an existence proof of the viability of an evolutionary process as a model for design. This thesis lays the foundations for the use of genetic algorithms in helping to attack a well-defined and important subset of design. It maps genetic algorithms onto the design process; defines appropriate representation criteria to take advantage of the nature of the problem; specifies methods of analyzing genetic-algorithm-generated designs; and places bounds on the time complexity of the task. Scalable examples from circuit design, floorplanning and function optimization are used to demonstrate, illustrate and ground these results.

Hill-climbing, simulated annealing and genetic algorithms: a comparative study and application to the mapping problem

Abstract: Hill-climbing, simulated annealing and genetic algorithms are search techniques that can be applied to most combinatorial optimization problems. The three algorithms are used to solve the mapping problem, which is the optimal static allocation of communication processes on distributed memory architectures. Each algorithm is independently evaluated and optimized according to its parameters. The parallelization of the algorithms is also considered. As an example, a massively parallel genetic algorithm is proposed for the problem, and results of its implementation on a 128-processor Supernode are given. A comparative study of the algorithms is then carried out. The criteria of performance considered are the quality of the solutions obtained and the amount of search time used for several benchmarks. A hybrid approach consisting of a combination of genetic algorithms and hill-climbing is also proposed and evaluated. >

Recombination Distributions for Genetic Algorithms

Abstract: Though genetic algorithms are loosely based on the principles of genetic variation and natural selection, the theory of mathematical genetics has not played a large role in most analyses of genetic algorithms. This paper reviews some well known results in mathematical genetics that use probability distributions to characterize the effects of recombination on multiple loci in the absence of selection. The relevance of this characterization to genetic algorithm research is illustrated by using it to quantify certain inductive biases associated with crossover operators. The potential significance of this work for the theory of genetic algorithms is discussed.

Dynamic Control of Genetic Algorithms in a Noisy Environment

Abstract: In this paper, we present e cient algorithms for adjusting con guration parameters of genetic algorithms that operate in a noisy environment. Assuming that the population size is given, we address two problems speci cally important in a noisy environment. First, we study the duration-sizing problem that determines dynamically the duration of each generation. Next, we study the sample-allocation (sizing) problem that determines adaptively the number of evaluations taken from each population in a generation. For these two problems, we model the search process as a statistical selection process and derive equations useful for controlling the duration and the sample sizes. Our result shows that these adaptive procedures improve the performance of genetic algorithms over those of commonly used static ones.