18. Genetic and evolutionary algorithms

The power system blackout history of last two decades is presented.Conventional load shedding techniques, their types and limitations are presented.Applications of intelligent techniques in load shedding are presented.Intelligent techniques include ANN, fuzzy logic, ANFIS, genetic algorithm and PSO.The discussion and comparison between these techniques are provided.Recent blackouts around the world question the reliability of conventional and adaptive load shedding techniques in avoiding such power outages. To address this issue, reliable techniques are required to provide fast and accurate load shedding to prevent collapse in the power system. Computational intelligence techniques, due to their robustness and flexibility in dealing with complex non-linear systems, could be an option in addressing this problem. Computational intelligence includes techniques like artificial neural networks, genetic algorithms, fuzzy logic control, adaptive neuro-fuzzy inference system, and particle swarm optimization. Research in these techniques is being undertaken in order to discover means for more efficient and reliable load shedding. This paper provides an overview of these techniques as applied to load shedding in a power system. This paper also compares the advantages of computational intelligence techniques over conventional load shedding techniques. Finally, this paper discusses the limitation of computational intelligence techniques, which restricts their usage in load shedding in real time.

International Journal on Bioinformatics & Biosciences (IJBB) is a Quarterly peer-reviewed and refereed open access journal that publishes articles which contribute new results in all areas of the Bioinformatics & Biosciences. Bioinformatics is the application of information science and technology to the field of biology to increase the understanding of all biological process. The aim of this journal is to publish all the latest and outstanding research articles in all areas of bioinformatics and Biometrics. Researchers and scientists from the fields of biology, computer science, mathematics, statistics, and physics are invited to share their developments and new techniques in the fields of Biometrics and Bioinformatics.

Technological innovations in soft computing techniques have brought automation capabilities to new levels of applications. Process control is an important application of any industry for controlling the complex system parameters, which can greatly benefit from such advancements. Conventional control theory is based on mathematical models that describe the dynamic behaviour of process control systems. Due to lack in comprehensibility, conventional controllers are often inferior to the intelligent controllers. Soft computing techniques provide an ability to make decisions and learning from the reliable data or expert's experience. Moreover, soft computing techniques can cope up with a variety of environmental and stability related uncertainties. This paper explores the different areas of soft computing techniques viz. Fuzzy logic, genetic algorithms and hybridization of two and abridged the results of different process control case studies. It is inferred from the results that the soft computing controllers provide better control on errors than conventional controllers. Further, hybrid fuzzy genetic algorithm controllers have successfully optimized the errors than standalone soft computing and conventional techniques.

Evolutionary Algorithms (EA) consist of several heuristics, which are able to solve optimisation tasks by imitating some aspects of natural evolution. They may use different levels of abstraction, but they are always working on whole populations of possible solutions for a given task. EAs are an approved set of heuristics, which are flexible to use and postulate only negligible requirements on the optimization task.

The use of Genetic Algorithms in evolution of mutants and test cases offers new possibilities in addressing some of the main problems of mutation testing. Most specifically the problem of equivalent mutant detection, and the problem of the large number of mutants produced. In this paper we describe the above problems in detail and introduce a new methodology based on co-evolutionary search techniques using Genetic Algorithms in order to address them effectively. Co-evolution allows the parallel evolution of mutants and test cases. We ...

Appropriate planning of resources is of critical importance to supply chain operation, often determining its success or failure. In this research we take an Interval Type-2 Fuzzy Logic supply chain model, and use a series of evolutionary algorithms to find good solutions. Experiments are conducted with a set of configurations to discover the most suitable for the task. A discussion of the results is given along with considerations for future research.

The use of Genetic Algorithms in evolution of mutants and test cases offers new possibilities in addressing some of the main problems of mutation testing. Most specifically the problem of equivalent mutant detection, and the problem of the large number of mutants produced. In this paper we describe the above problems in detail and introduce a new methodology based on co-evolutionary search techniques using Genetic Algorithms in order to address them effectively. Co-evolution allows the parallel evolution of mutants and test cases. We ...

VLSI design constraints are always area, power and delay. Power consumption of VLSI has become a leading design concern with the growth of complexity and density. Leakage power reduces battery life for the entire portable battery operated device such as mobile phones, laptop and camcoder and so on. Reduction of leakage power is of peak concern in the present trend of nanotechnology. Many techniques have been projected to reduce the leakage power consumption, but most of these approaches require the process technology support. Input Vector Control is one of the approaches used for static power reduction in standby mode. Leakage in a circuit depends on input vector applied at primary inputs due to stack effect. Minimum leakage vector is the input vector to which a circuit can suggest a minimum leakage for a certain set of test patterns. This paper presents Minimum Leakage Vector for various test circuits using genetic algorithm.

... of Evolution David M. Howard, Andy M. Tyrrell, and Crispin Cooper Department of Electronics, University of York, York, UK ... [17] Howard, DM, Welch, GF, Brereton, J., Himonides, E., DeCosta, M., Williams, J. and Howard, AW WinSingad: A real-time display for the singing studio. ...

Scope 8 th International Conference on Control, Modeling and Computing (CMC 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Control Engineering, Modeling, Computing and Applications. The goal of this Conference is to bring together researchers and practitioners from academia and industry to focus on understanding modern Control Engineering, Modeling and Computing concepts and establishing new collaborations in these areas.

Scope 4 th International Conference on Soft Computing, Control and Mathematics (SCM 2021) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications impacts and challenges of Soft Computing, Mathematics and Control. The conference documents practical and theoretical results which make a fundamental contribution for the development of Soft Computing, Mathematics and Control. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

Following the work of Stephens and coworkers on the coarse-grained dynamics of genetic systems, we work towards a possible generalisation in the context of genetic algorithms, giving as examples schemata, genotype-phenotype mappings, and error classes in the Eigen model. We discuss how the dynamics transforms under a coarse-graining, comparing and contrasting different notions of invariance. We work out some examples in the two-bit case, to illustrate the ideas and issues. We then find a bound for the Selection Weighted Linkage Disequilibrium Coefficient for the two-bit onemax problem.

With the growing number of textual resources available, the ability to understand them becomes critical.
An essential first step in understanding these sources is the ability to identify the parts-of-speech in each
sentence. Arabic is a morphologically rich language, which presents a challenge for part of speech
tagging. In this paper, our goal is to propose, improve, and implement a part-of-speech tagger based on a
genetic algorithm. The accuracy obtained with this method is comparable to that of other probabilistic
approaches.