Interactive Markov Models of Evolutionary Algorithms

This paper introduces a Markov model for evolutionary algorithms (EAs) that is based on interactions among individuals in the population. This interactive Markov model has the potential to provide tractable models for optimization problems of realistic size. We propose two simple discrete optimization search strategies with population-proportion-based selection and a modified mutation operator. The probability of selection is linearly proportional to the number of individuals at each point of the search space. The mutation operator randomly modifies an entire individual rather than a single decision variable. We exactly model these optimization search strategies with interactive Markov models. We present simulation results to confirm the interactive Markov model theory. We show that genetic algorithms and biogeography-based optimization perform better with the addition of population-proportion-based selection on a set of real-world benchmarks. We note that many other EAs, both new and old, might be able to be improved with this addition, or modeled with this method

Interactive Markov Models of Evolutionary Algorithms

This paper introduces a Markov model for evolutionary algorithms (EAs) that is based on interactions among individuals in the population. This interactive Markov model has the potential to provide tractable models for optimization problems of realistic size. We propose two simple discrete optimization search strategies with population-proportion-based selection and a modified mutation operator. The probability of selection is linearly proportional to the number of individuals at each point of the search space. The mutation operator randomly modifies an entire individual rather than a single decision variable. We exactly model these optimization search strategies with interactive Markov models. We present simulation results to confirm the interactive Markov model theory. We show that genetic algorithms and biogeography-based optimization perform better with the addition of population-proportion-based selection on a set of real-world benchmarks. We note that many other EAs, both new and old, might be able to be improved with this addition, or modeled with this method

A Framework for Estimating the Applicability of GAs for Real‐World Optimization Problems

This paper introduces a methodology for estimating the applicability of a particular Genetic Algorithm (GA) configuration for an arbitrary optimization problem based on run-time data. GAs are increasingly employed to solve complex real-world optimization problems featuring ill-behaved search spaces (e.g., non-continuous, non-convex, non-differentiable) for which traditional algorithms fail. The quality of the optimal solution (i.e., the fitness value of the global optimum) is typically unknown in a real-world problem, making it hard to assess the absolute performance of an algorithm which is being applied to that problem. In other words, with a solution provided by a GA run, there generally lacks a method or a theory to measure how good the solution is. Although many researchers applying GAs have provided experimental results showing their successful applications, those are merely averaged-out, \emph{ad hoc} results. The results cannot represent nor guarantee the usability of the best solutions obtained from a single GA run since the solutions can be very different for each run. Therefore, it is desirable to provide a formalized measurement to estimate the applicability of GAs to real-world problems. This work extends our earlier work on the convergence rate, and proposes an evaluation metric to quantify the applicability of GAs. Through this metric, a degree of convergence can be obtained after each GA run so that researchers and practitioners are able to obtain certain information about the relation between the best solution and all of the feasible solutions. To support the proposed evaluation metric, a series of theorems are formulated from the theory of matrices. Moreover, several experiments are conducted to validate the metric

The application of manifold based visual speech units for visual speech recognition

This dissertation presents a new learning-based representation that is referred to as a Visual Speech Unit for visual speech recognition (VSR). The automated recognition of human speech using only features from the visual domain has become a significant research topic that plays an essential role in the development of many multimedia systems such as audio visual speech recognition(AVSR), mobile phone applications, human-computer interaction (HCI) and sign language recognition. The inclusion of the lip visual information is opportune since it can improve the overall accuracy of audio or hand recognition algorithms especially when such systems are operated in environments characterized by a high level of acoustic noise. The main contribution of the work presented in this thesis is located in the development of a new learning-based representation that is referred to as Visual Speech Unit for Visual Speech Recognition (VSR). The main components of the developed Visual Speech Recognition system are applied to: (a) segment the mouth region of interest, (b) extract the visual features from the real time input video image and (c) to identify the visual speech units. The major difficulty associated with the VSR systems resides in the identification of the smallest elements contained in the image sequences that represent the lip movements in the visual domain. The Visual Speech Unit concept as proposed represents an extension of the standard viseme model that is currently applied for VSR. The VSU model augments the standard viseme approach by including in this new representation not only the data associated with the articulation of the visemes but also the transitory information between consecutive visemes. A large section of this thesis has been dedicated to analysis the performance of the new visual speech unit model when compared with that attained for standard (MPEG- 4) viseme models. Two experimental results indicate that: 1. The developed VSR system achieved 80-90% correct recognition when the system has been applied to the identification of 60 classes of VSUs, while the recognition rate for the standard set of MPEG-4 visemes was only 62-72%. 2. 15 words are identified when VSU and viseme are employed as the visual speech element. The accuracy rate for word recognition based on VSUs is 7%-12% higher than the accuracy rate based on visemes