Bioinspired Algorithms in Solving Three-Dimensional Protein Structure Prediction Problems

Proteins play a vital molecular role in all living organisms. Experimentally, it is difficult to predict the protein structure, however alternatively theoretical prediction method holds good for it. The 3D structure prediction of proteins is very much important in biology and this leads to the discovery of different useful drugs, enzymes, and currently this is considered as an important research domain. The prediction of proteins is related to identification of its tertiary structure. From the computational point of view, different models (protein representations) have been developed along with certain efficient optimization methods to predict the protein structure. The bio-inspired computation is used mostly for optimization process during solving protein structure. These algorithms now a days has received great interests and attention in the literature. This chapter aim basically for discussing the key features of recently developed five different types of bio-inspired computational algorithms, applied in protein structure prediction problems.

Gene Expression Programming

Gene expression programming (GEP) introduced by Candida Ferreira is a descendant of genetic algorithm (GA) and genetic programming (GP). It takes the advantage of both the optimization and search technique based on genetics and natural selection as GA and its programmatic Darwinian counterpart GP. It is gaining popularity because; it has to some extent eradicated the ‘cons' of both while keeping in the ‘pros'. It is still a new technique not much explored since its introduction in 2001. In this chapter both GA and GP is first discussed followed by the elaborate discussion of GEP. This is followed up by the discussion on research work done is different fields using GEP as a tool followed up by GEP architectures. Finally, here GEP has been used for detection of age from facial features as a soft computing based optimization problem using genetic operators.

Classification of Faults in Power Transmission Systems Using Modern Techniques

Power system constitute a major part of the electrical system relating in the present world. Every single portion of this system assumes a major part in the accessibility of the electrical power one uses at their homes, enterprises, workplaces, industrial facilities and so on. Any deficiency in power system causes a ton of inconvenience for the maintenance of the system. So transmission system needs a proper protection scheme to ensure continuous power supply to the consumers. The countless extent of power systems and applications requires the improvement in suitable techniques for the fault classification in power transmission systems, to increase the efficiency of the systems and to avoid major damages. For this purpose, the technical literature proposes a large number of methods. This chapter analyzes the technical literature, summarizing the most important methods that can be applied to fault classification and advanced technologies developed by various researchers in power transmission systems.

Performance Analysis of Classifiers on Filter-Based Feature Selection Approaches on Microarray Data

The process of Feature selection in machine learning involves the reduction in the number of features (genes) and similar activities that results in an acceptable level of classification accuracy. This paper discusses the filter based feature selection methods such as Information Gain and Correlation coefficient. After the process of feature selection is performed, the selected genes are subjected to five classification problems such as Naïve Bayes, Bagging, Random Forest, J48 and Decision Stump. The same experiment is performed on the raw data as well. Experimental results show that the filter based approaches reduce the number of gene expression levels effectively and thereby has a reduced feature subset that produces higher classification accuracy compared to the same experiment performed on the raw data. Also Correlation Based Feature Selection uses very fewer genes and produces higher accuracy compared to Information Gain based Feature Selection approach.

Significance of Biologically Inspired Optimization Techniques in Real-Time Applications

The techniques inspired from the nature based evolution and aggregated nature of social colonies have been promising and shown excellence in handling complicated optimization problems thereby gaining huge popularity recently. These methodologies can be used as an effective problem solving tool thereby acting as an optimizing agent. Such techniques are called Bio inspired computing. Our study surveys the recent advances in biologically inspired swarm optimization methods and Evolutionary methods, which may be applied in various fields. Four real time scenarios are demonstrated in the form of case studies to show the significance of bio inspired algorithms. The techniques that are illustrated here include Differential Evolution, Genetic Search, Particle Swarm optimization and artificial bee Colony optimization. The results inferred by implanting these techniques are highly encouraging.

Generating Efficient Techniques for Information Extraction and Processing Using Cellular Automata

The evolution of Cellular automaton has proved to be very efficient in carrying out arbitrary information processing. A significant application lies in the theory and practice of finding a technique for unifying the information processing. But, in this case the structures used in conventional computer languages are largely inappropriate. The definite organization of computer memory into named areas, stacks, and so on, is not suitable for cellular automata in which processing elements are not distinguished from memory elements. Rather it can be assumed that the data could be represented by an object like a graph, on which transformations can be performed in parallel. This chapter initiate with basic literature on cellular automata, related definitions and notations and focuses on its applications in information processing.

Swarm-Based Clustering for Gene Expression Data

Clustering is one of the most important techniques, which group genes of similar expression pattern into a small number of meaningful homogeneous groups or clusters. Gene expression data has certain special characteristics and is a challenging research problem. There are many applications for clustering gene expression data. Clustering can be applied for genes called gene clustering. Hard clustering allows a gene to get placed in exactly one cluster and converges in local optima. Soft clustering approach allows gene to get placed in all the clusters with some membership values. As the hard clustering approach converges in local optimum, an evolutionary computation technique like swarm clustering is required to find the global optimum solution. This chapter studies swarm clustering techniques such as Particle Swarm Clustering K-Means, Cuckoo Search Clustering, Cuckoo Search Clustering with levy flight, harmony search, Fuzzy PSO and Ant Colony Optimization based Clustering for clustering gene expression data. Evaluation measures for clustering gene expression data are also discussed.

Bio-Inspired Techniques in Rehabilitation Engineering for Control of Assistive Devices

The intelligent control of assistive devices is possible from bio-signals or gestures to find the user's intention. The goal of the user intention recognition system is to develop computational methods for decoding the acquired bio-signal data. One of the methods of accomplishing the objective will be using the pattern recognition system. The study of higher level control of assistive device using various data processing techniques with bio-inspired techniques is in progress. The knowledge of bio-inspired computation is essential for the neophytes to develop algorithms for identification of intention from bioelectric signals. Most literatures, demonstrates the application using signals and not much definite study describes the various bio-inspiring computation involved to develop the control of assistive devices in real-time. Therefore, this chapter presents a brief survey of the various bio-inspiring techniques used in interfacing devices for identification of information from the user intends.

A Novel Hybrid Genetic Algorithm for Unconstrained and Constrained Function Optimization

During the past decade, academic and industrial communities are highly interested in evolutionary techniques for solving optimization problems. Genetic Algorithm (GA) has proved its robustness in solving all most all types of optimization problems. To improve the performance of GA, several modifications have already been done within GA. Recently GA has been hybridized with many other nature-inspired algorithms. As such Bacterial Foraging Optimization (BFO) is popular bio inspired algorithm based on the foraging behavior of E. coli bacteria. Many researchers took active interest in hybridizing GA with BFO. Motivated by such popular hybridization of GA, an attempt has been made in this chapter to hybridize GA with BFO in a novel fashion. The Chemo-taxis step of BFO plays a major role in BFO. So an attempt has been made to hybridize Chemo-tactic step with GA cycle and the algorithm is named as Chemo-inspired Genetic Algorithm (CGA). It has been applied on benchmark functions and real life application problem to prove its efficacy.

Bio-Inspired Algorithms for Text Summarization

In last few decades, Bio-inspired algorithms (BIAs) have gained a significant popularity to handle hard real world and complex optimization problem. The scope and growth of Bio Inspired algorithms explore new application areas and computing opportunities. This paper presents a review with the objective is to bring a better understanding and to motivate the research on BIAs based text summarization. Different techniques have been used for text summarization are genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), harmonic search (HS).