K.F. Man

Genetic algorithms and their applications

This article introduces the genetic algorithm (GA) as an emerging optimization algorithm for signal processing. After a discussion of traditional optimization techniques, it reviews the fundamental operations of a simple GA and discusses procedures to improve its functionality. The properties of the GA that relate to signal processing are summarized, and a number of applications, such as IIR adaptive filtering, time delay estimation, active noise control, and speech processing, that are being successfully implemented are described.

Generating chaos in Chua's circuit via time-delay feedback

A time-delay chaotification approach can be applied to the Chuas circuit by adding a small-amplitude time-delay feedback voltage to the circuit. The chaotic dynamics of this newly derived time-delay Chuas circuit is studied by theoretical analysis, verified by computer simulations as well as by circuit experiments.

Multiobjective Coordinated Power Voltage Control Using Jumping Genes Paradigm

A new jumping genes paradigm in the format of hierarchical genetic algorithm is proposed for optimizing the power voltage control systems. The advantage of this scheme is its unique capacity for finding the required solutions that can be used for stabilizing the progressive voltage drop or even voltage collapse of a power system. Because of the multiobjective classification, all these solutions could therefore form a landscape of control pattern which is aptly applicable to the control purpose of the coordinated voltage control system. The application of the proposed paradigm is verified upon the New England 39-bus power system. A tradeoff between the recovery time and the quality of voltage control exists and largely depends upon the number of steps for control actions.

A Theoretical Development and Analysis of Jumping Gene Genetic Algorithm

Recently, gene transpositions have gained their power and attentions in computational evolutionary algorithm designs. In 2004, the Jumping Gene Genetic Algorithm (JGGA) was first proposed and two new gene transposition operations, namely, cut-and-paste and copy-and-paste, were introduced. Although the outperformance of JGGA has been demonstrated by some detailed statistical analyses based on numerical simulations, more rigorous theoretical justification is still in vain. In this paper, a mathematical model based on schema is derived. It then provides theoretical justifications on why JGGA is superiority in searching, particularly when it is applied to solve multiobjective optimization problems. The studies are also further verified by solving some optimization problems and comparisons are made between different optimization algorithms.

Parallel Genetic-Based Hybrid Pattern Matching Algorithm for Isolated Word Recognition

Dynamic Time Warping (DTW) is a common technique widely used for nonlinear time normalization of different utterances in many speech recognition systems. Two major problems are usually encountered when the DTW is applied for recognizing speech utterances: (i) the normalization factors used in a warping path; and (ii) finding the K-best warping paths. Although DTW is modified to compute multiple warping paths by using the Tree-Trellis Search (TTS) algorithm, the use of actual normalization factor still remains a major problem for the DTW. In this paper, a Parallel Genetic Time Warping (PGTW) is proposed to solve the above said problems. A database extracted from the TIMIT speech database of 95 isolated words is set up for evaluating the performance of the PGTW. In the database, each of the first 15 words had 70 different utterances, and the remaining 80 words had only one utterance. For each of the 15 words, one utterance is arbitrarily selected as the test template for recognition. Distance measure for each test template to the utterances of the same word and to those of the 80 words is calculated with three different time warping algorithms: TTS, PGTW and Sequential Genetic Time Warping (SGTW). A Normal Distribution Model based on Rabiner23 is used to evaluate the performance of the three algorithms analytically. The analyzed results showed that the PGTW had performed better than the TTS. It also showed that the PGTW had very similar results as the SGTW, but about 30% CPU time is saved in the single processor system.

Multiobjective optimization of radio-to-fiber repeater placement a jumping gene algorithm

This paper considers the radio-to-fiber repeater placement problem in wireless local loop (WLL) Systems. The severe problem that the WLL systems encountered is that the large diffraction loss from rooftop to street occurs at its frequency band, 2.3 GHz. The radio-to-fiber repeaters can be used for the remedy of this situation. Unlike the conventional WLL systems, the total system cost of this option depends on the additional repeaters and optical fibers (links). Thus, our objective is to minimize the total repeater cost and total link cost simultaneously by selecting optimal locations for the repeaters. It is a multiobjective problem in which a tradeoff between the total repeater cost and total link cost can thus be made. A new jumping gene paradigm called jumping-gene genetic algorithm (JGGA) is proposed to solve this conflicting dilemma. The main feature of JGGA is that it only consists of a simple operation in which a transposition of the gene(s) is induced within the same or another chromosome within the framework of genetic algorithm. The algorithm has been tested by using two specific performance metrics in evaluating the quality of obtained sets of non-dominated solutions. Simulation results revealed from this study that JGGA is able to find non-dominated solutions with better convergence and diversity than other multiobjective evolutionary algorithms.

Recurrent NN model for chaotic time series prediction

A new Elman neural network learning algorithm is proposed for chaotic time series prediction. This method has a number of advantages over the use of a standard backpropagation algorithm. It is not only its capability for handling a much higher complexity time data series, but its superiority in time convergence can prove to be a valuable asset for time critical applications. Furthermore, this method is also very accurate in prediction as it can reach global minimum in a much attainable manner.

A Multiple Criteria Decision-Making Knowledge-Based Scheme for Real-Time Power Voltage Control

A new real-time power voltage control strategy is proposed in this paper. This scheme utilizes a novel offline evolutionary multiobjective optimization algorithm called jumping genes for generating the widespread control solutions and be readily stored into a knowledge data base. A separate online multiple criteria decision-making scheme is established for selecting the appropriate control solution. This concept of power voltage control has been demonstrated by the New England 39-bus power system. The system output performance was speedy and accurate.

GA Approach to Multiple Objective Optimization for Active Noise Control

Abstract In this paper, a novel system is proposed to obtain global noise control within an enclosure. Instead of using the traditional least squares method for the controller design, the emerging technology of Genetic Algorithms is applied for the design of an optimal multi-channels active noise control system. The advantage of this system is that it has the ability to tackle a multi-objectives error function problem so that a global noise reduction is achieved. Experimental result has illustrated that it is effective for achieving global quietness for an enclosed environment.

Teleoperation controller design using hierarchical genetic algorithm

Hierarchical genetic algorithm (HGA) is formulated to design the H/sub /spl infin// controller for the teleoperation system used in microsurgery. Such a HGA is capable of searching the structure of the H/sub /spl infin// and its coefficients simultaneously. A multiple objective ranking scheme is also incorporated so that both the force and position transparency are optimized against any strictly passive environment.