Application Of Evolutionary Computation Research Articles

A BIBLIOGRAPHICAL SURVEY OF EVOLUTIONARY COMPUTATION APPLICATIONS IN POWER SYSTEMS (1994–2003)

This article presents an extended bibliography of evolutionary computation applications in power systems and briefly discusses applications of evolutionary computation to various power system problems

Application of Evolutionary Computation in Automotive Powertrain Mount Tuning

Engine mount tuning is a multi‐disciplinary exercise since it affects Idle‐shake, Road‐shake and power‐train noise response. Engine inertia is often used as a tuned absorber for controlling suspension resonance related road‐shake issues. Last but not least, vehicle ride and handling may also be affected by mount tuning. In this work, Torque‐Roll‐Axis (TRA) decoupling of the rigid powertrain was used as a starting point for mount tuning. Nodal point of flexible powertrain bending was used to define the envelop for transmission mount locations. The frequency corresponding to the decoupled roll mode of the rigid powertrain was then adjusted for idle‐shake and road‐shake response management.A TRA decoupling procedure, cast as a multi‐objective optimization problem, was applied to a body‐on‐frame sport‐utility vehicle powertrain system. In addition to a standard gradient based optimization algorithm, available in commercial finite element software, an evolutionary computation paradigm known as Evolutionary Strategies (ES) was used to solve the optimization problem. The primary advantages of evolutionary computation over gradient based algorithms are as follows: i) They are less likely to get trapped in local minima and less dependent on initial values of the design parameters and therefore able to handle multi‐modal optimization problems unlike gradient based algorithms, ii) They produce a population of viable solutions, unlike gradient based algorithms which yields a single solution. The second advantage is very attractive in a production environment since packaging and other multi‐disciplinary constraints often require multiple quality solutions for the same problem. The process outlined in this work was verified by exercising a full‐vehicle finite element model. The process produced a set of production feasible powertrain mount parameters for acceptable idle and road shake performance.

Evolutionary computation and structural design: A survey of the state-of-the-art

Evolutionary computation is emerging as a new engineering computational paradigm, which may significantly change the present structural design practice. For this reason, an extensive study of evolutionary computation in the context of structural design has been conducted in the Information Technology and Engineering School at George Mason University and its results are reported here. First, a general introduction to evolutionary computation is presented and recent developments in this field are briefly described. Next, the field of evolutionary design is introduced and its relevance to structural design is explained. Further, the issue of creativity/novelty is discussed and possible ways of achieving it during a structural design process are suggested. Current research progress in building engineering systems’ representations, one of the key issues in evolutionary design, is subsequently discussed. Next, recent developments in constraint-handling methods in evolutionary optimization are reported. Further, the rapidly growing field of evolutionary multiobjective optimization is presented and briefly described. An emerging subfield of coevolutionary design is subsequently introduced and its current advancements reported. Next, a comprehensive review of the applications of evolutionary computation in structural design is provided and chronologically classified. Finally, a summary of the current research status and a discussion on the most promising paths of future research are also presented.

Special Issue on Selected Papers in SCIS & ISIS 2004 - No.1

The Joint Conference of the 2nd International Conference on Soft Computing and Intelligent Systems and the 5th International Symposium on Advanced Intelligent Systems (SCIS & ISIS 2004) was held at Keio University in Yokohama, Japan, on September 21-24, 2004. Over 300 papers in various fields, for example, mathematics, urban and transport planning, entertainment, intelligent control, learning, image processing, clustering, neural networks application, evolutionary computation, system modeling, fuzzy measures, and robotics were submitted to the conference. The Program Committee required reviewers in SCIS & ISIS 2004 to select excellent papers considering publication in a special issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII). Some 45 papers were selected and some of them accepted by other reviewers will be published in a two-part special issue of SCIS & ISIS 2004. In this, the first part, 13 papers have been classified into six groups — papers 1-3 under intelligent control, paper 4 under robotics, papers 5 and 6 under neural network applications, papers 7-9 under evolutionary computation applications, paper 10 under human behavior analysis, and papers 11-13 under image processing. Remaining papers currently under review will be published in the next volume. We thank the reviewers for their time and effort in making these special issues possible so quickly, and thank the JACIII editorial board, especially Profs. Hirota and Fukuda, the Editors-in-Chief and Managing Editor Kenta Uchino for their invaluable aid and advice in putting these special issues together. This issue is dedicated to the late Prof. Toshiro Terano, who passed away on February 15, 2005. He will be greatly missed.

EVOLUTIONARY ALGORITHMS IN CONTROL SYSTEM ENGINEERING

This paper introduces the session describing the state-of-the-art in the application of evolutionary computation in control system engineering. Evolutionary methods such as genetic algorithms (GAs) and genetic programming (GP) are particularly suitable in problems for which conventional optimizers are inefficient or inappropriate, rather than simply as an alternative to conventional optimization. This session will be of interest to the control engineering community as a whole, and will provide an educational background to those not familiar with these methods as well as presenting new results and applications.

Evolutionary computing in manufacturing industry: an overview of recent applications

Traditional methods often employed to solve complex real world problems tend to inhibit elaborate exploration of the search space. They can be expensive and often results in sub-optimal solutions. Evolutionary computation (EC) is generating considerable interest for solving real world engineering problems. They are proving robust in delivering global optimal solutions and helping to resolve limitations encountered in traditional methods. EC harnesses the power of natural selection to turn computers into optimisation tools. The core methodologies of EC are genetic algorithms (GA), evolutionary programming (EP), evolution strategies (ES) and genetic programming (GP). This paper attempts to bridge the gap between theory and practice by exploring characteristics of real world problems and by surveying recent EC applications for solving real world problems in the manufacturing industry. The survey outlines the current status and trends of EC applications in manufacturing industry. For each application domain, the paper describes the general domain problem, common issues, current trends, and the improvements generated by adopting the GA strategy. The paper concludes with an outline of inhibitors to industrial applications of optimisation algorithms.

Voice quality conversion using interactive evolution of prosodic control

Recent years have seen the birth of new markets using voice quality conversion technology for a variety of application fields including man-personal machine interfaces, addition of narration in multimedia-content editing, and computer games. Optimal parameters for voice quality conversion, however, are speaker dependent, and consequently, no clear-cut algorithm has existed in the past and parameter adjustment has usually been performed by an experienced designer on a trial and error basis. This paper proposes the application of evolutionary computation, a stochastic search technique based on organic evolution, to parameter adjustment for voice conversion, and reports on several experimental results applicable to the fitting of prosodic coefficients. Evolutionary computation is said to be “applicable to even cases where the properties of the target function are not well known,” and we decided to apply it considering that this feature might be effective in our study. Providing an explicit evaluative function for evolutionary computation, however, is difficult, and we here adopt an interactive-evolution system in which genetic manipulation is performed repeatedly while evaluating results based on human emotions. Evaluation experiments were performed on raw human speech recorded by a microphone and speech mechanically synthesized from text. It was found that the application of evolutionary computation could achieve voice conversion satisfying specific targets with relatively little degradation of sound quality and no impression of artificial processing in comparison to parameter adjustment based on designer experience or trial and error. This paper also shows that prosodic conversion coefficients determined by the interactive evolution technique, while exhibiting speaker dependency, is not text dependent.

Introduction to the Applications of Evolutionary Computation in Computer Security and Cryptography

Techniques taken from the field of Artificial Intelligence, especially Evolutionary Computation (Genetic Algorithms and Genetic Programming, but also others) are steadily becoming more and more present in the area of computer security, both in network/host security and in the very demanding area of cryptology. In recent years, many algorithms that take advantage of approaches based on Evolutionary Computation have been proposed, for example, in the design and analysis of a number of new cryptographic primitives, ranging from pseudo-random number generators to block ciphers, in the cryptanalysis of state-of-the-art cryptosystems, and in the detection of network attacking patterns, to name a few. There is a growing interest from the computer security community toward Evolutionary Computation techniques, as a result of these recent successes, but there still are a number of open problems in the field that should be addressed.

At the crossroads of evolutionary computation and music: self-programming synthesizers, swarm orchestras and the origins of melody.

This paper introduces three approaches to using Evolutionary Computation (EC) in Music (namely, engineering, creative and musicological approaches) and discusses examples of representative systems that have been developed within the last decade, with emphasis on more recent and innovative works. We begin by reviewing engineering applications of EC in Music Technology such as Genetic Algorithms and Cellular Automata sound synthesis, followed by an introduction to applications where EC has been used to generate musical compositions. Next, we introduce ongoing research into EC models to study the origins of music and detail our own research work on modelling the evolution of melody.

Evolutionary approaches to fuzzy modelling for classification

An overview of the application of evolutionary computation to fuzzy knowledge discovery is presented. This is set in one of two contexts: overcoming the knowledge acquisition bottleneck in the development of intelligent reasoning systems, and in the data mining of databases where the aim is the discovery of new knowledge. The different strategies utilizing evolutionary algorithms for knowledge acquisition are abstracted from the work reviewed. The simplest strategy runs an evolutionary algorithm once, while the iterative rule learning approach runs several evolutionary algorithms in succession, with the output from each considered a partial solution. Ensembles are formed by combining several classifiers generated by evolutionary techniques, while co-evolution is often used for evolving rule bases and associated membership functions simultaneously. The associated strengths and limitations of these induction strategies are compared and discussed. Ways in which evolutionary techniques have been adapted to satisfy the common evaluation criteria of the induced knowledge—classification accuracy, comprehensibility and novelty value—are also considered. The review concludes by highlighting common limitations of the experimental methodology used and indicating ways of resolving them.

A review on the application of evolutionary computation to information retrieval

In this contribution, different proposals found in the specialized literature for the application of evolutionary computation to the field of information retrieval will be reviewed. To do so, different kinds of IR problems that have been solved by evolutionary algorithms are analyzed. Some of the specific existing approaches will be specifically described for some of these problems and the obtained results will be critically evaluated in order to give a clear view of the topic to the reader.

Uma abordagem evolutiva para geração automática de turnos completos em torneios

Este artigo apresenta contribuições junto à solução de problemas de escalonamento, mais precisamente na geração de turnos completos em torneios. Trata-se de um problema de grande interesse prático, caracterizado por questões de factibilidade e uma explosão combinatória de candidatos à solução. Sendo assim, a atuação direta de um especialista e a aplicação de ferramentas convencionais de busca geralmente não conduzem a resultados satisfatórios. A estratégia de solução proposta está baseada na aplicação conjunta de computação evolutiva, busca local e otimização baseada em restrições. Embora outras abordagens evolutivas já tenham sido propostas na literatura, a empregada aqui inova ao sugerir uma representação genética compacta aliada a um algoritmo de expansão de código. Comparadas às soluções já implementadas para problemas reais de escalonamento, aquelas obtidas a partir da estratégia de solução proposta neste trabalho apresentaram melhor desempenho e a quantidade de recursos computacionais requeridos para produzir a solução é aceitável. A aplicação conjunta de computação evolutiva, busca local e técnicas de otimização baseada em restrições pode ser estendida ao tratamento de outros problemas de escalonamento, supondo a existência de uma codificação genética compacta e a disponibilidade de um algoritmo de otimização baseado em restrições.

Application of Evolutionary Computation to Optimal On-ramp Traffic Control

In this paper, the numerical solution method of an on-ramp traffic control problem is discussed from the viewpoint of the application of evolutionary computation. The problem is formulated as a discrete-time nonlinear optimal control problem. The optimal control problem is then transferred to an optimization problem with successive constraints. A parallel-adaptive genetic algorithm is proposed to obtain the numerical solution of the problem. A real-time practical control method is also proposed. Numerical simulations for assessing the performance of the proposed methods are performed using observed data of Hanshin Expressway. As a result, the proposed method obtains the solution at comparatively small generations. This means shortening the calculation time, and is useful for the real-time control. Moreover, it is shown that the proposed real-time control is effective to reduce heavy congestion on the expressway.

Applying an evolutionary algorithm to telecommunication network design

This paper deals with the application of evolutionary computation to telecommunication network design. Design of a two-layer network is considered, where the upper-layer (UL) network uses resources of the lower-layer (LL) network. UL links determine demands for the LL and are implemented using LL paths (admissible paths). Within a fixed LL network topology, given the demands and admissible paths, we aim to find the LL link capacities for implementing the UL links, minimizing the cost of the LL. Robust design issues are also taken into consideration, allowing for failure of a certain part of the LL and postulating that, after some re-allocation in the LL, demands are still realized to an assumed extent. An algorithm based on an evolutionary technique is introduced, with problem-specific genetic operators to improve computing efficiency. A theoretical study of properties of the operators is made and several experiments are performed to tune the parameters of the algorithm. Finally, its performance is compared with other design techniques, including integer programming.

Evolutionary Computation in Structural Design

The paper provides the results of preliminary research on the application of evolutionary computation to integrated structural design in which a complex design support tool automatically conducts both conceptual and detailed design. In the paper, a brief overview of the state of the art in evolutionary computation and its applications to structural design is provided. Next, Inventor 2000 is described, a unique research and structural design tool developed by the authors at George Mason University that combines an evolutionary computation component with a system for wind forces analysis, and a system for the analysis, design and optimisation of steel structures. The paper also presents the results of four structural design experiments conducted with Inventor 2000. The objective of experiments was to investigate various forms of evolutionary computation as applied to structure design. Finally, the paper provides the initial research conclusions and recommendations for further research.

Adaptive Information Filtering using evolutionary computation

Information Filtering is concerned with filtering data streams in such a way as to leave only pertinent data (information) to be perused. When the data streams are produced in a changing environment the filtering has to adapt too in order to remain effective. Adaptive Information Filtering (AIF) is concerned with filtering in changing environments. The changes may occur both on the transmission side (the nature of the streams can change), and on the reception side (the interest of a user can change). Weighted trigram analysis is a quick and flexible technique for describing the contents of a document. A novel application of evolutionary computation is its use in Adaptive Information Filtering for optimizing various parameters, notably the weights associated with trigrams. The research described in this paper combines weighted trigram analysis, clustering, and a special two-pool evolutionary algorithm, to create an Adaptive Information Filtering system with such useful properties as domain independence, spelling error insensitivity, adaptability, and optimal use of user feedback while minimizing the amount of user feedback required to function properly. We designed a special evolutionary algorithm with a two-pool strategy for this changing environment.

Using evolutionary computational techniques in environmental modelling

Evolutionary Computation (EC) is a field of computer science that borrows concepts such as natural selection and the genotype-phenotype distinction from biology in order to solve a wide range of complex problems, such as robot controller design, job-shop schedule optimization, pattern recognition, electronic circuit design and many more. In addition, EC techniques in combination with individual-based modelling can be applied in their domain of origin, biology, to investigate the emergence and evolution of natural phenomena. This paper describes the use of EC as both (a) an empirical supplement to analytical approaches to mathematically tractable biological problems, and (b) a vital tool for analyzing highly complex systems of interacting species in heterogeneous environments. Three EC applications, two tractable and one complex, are used to illustrate these points. In general, this work introduces environmental modellers to a cutting-edge computer-science technique that can be of considerable utility, especially in a modern world in which accelerated rates of large-scale environmental change heighten the need for evolutionary considerations in analyses of relatively short time-scale phenomena.

Evolutionary computing: the spawning of a new generation

There is a new generation of young talent in the artificial intelligence field. It promises to take AI well into the new millenium with some innovative work. Some newcomers are forging the way beyond heuristic and mathematical approaches into other realms such as evolutionary computing. The author considers future applications of evolutionary computing.

The applications of evolutionary computation in software reliability

Software reliability models(SRMs) are the theoretic foundation of software reliability. However, the existence of intrinsic limitation of the preposition in traditional model building confirnes the applications of SRMs. In this paper, a new method evolutionary computation, is used to estimate parameters of SRMs. At the same time, new algorithms are also proposed and employed to build SRMs. As the experiment results demonstrate, evolutionary computation method is poweful and effective.

Evolutionary programming IV: Proceedings of the fourth annual conference on evolutionary programming

From the Publisher: March 1-3, 1995, San Diego, California Evolutionary programming is one of the predominate algorithms withing the rapidly expanding field of evolutionary computation. These edited contributions to the Fourth Annual Conference on Evolutionary Programming are by leading scientists from academia, industry, and defense. The papers describe both the theory and practical application of evolutionary programming, as well as other methods of evolutionary computation including evolution strategies, genetic algorithms, genetic programming, and cultural algorithms. Topics include: Novel Areas of Evolutionary Programming and Evolution Strategies. Evolutionary Computation with Medical Applications. Issues in Evolutionary Optimization Pattern Discovery, Pattern Recognition, and System Identification. Hierarchical Levels of Learning. Self-Adaptation in Evolutionary Computation. Morphogenic Evolutionary Computation. Issues in Evolutionary Optimization. Evolutionary Applications to VLSI and Part Placement. Applications of Evolutionary Computation to Biology and Biochemistry Control. Applications of Evolutionary Computation. Genetic and Inductive Logic Programming. Genetic Neural Networks. The Future of Evolutionary Computation. A Bradford Book. Complex Adaptive Systems series