Novel Algorithm for Hand Gesture Modeling Using Genetic Algorithm with Variable Length Chromosome

Many languages the people can exploit for them in order to communicate among them and get the message delivered, but, these languages should be known by those people in order to understand and speak, contrarily, gesture system is the common language that can be adopted for this objective and need less knowledge as compared with spoken languages that need the grammatically and semantically rules, in this paper we applied a novel algorithm for capturing hand gesture shape using one of the evolutionary algorithms in order to fit the hand segment. Previous techniques in the literature that fully captured hand shape applied some artificial intelligent methods [1] or some statistical methods [2]. Genetic Algorithms (GAs) with variable length of chromosomes is used to model the hand structure. The most effective GA parameters used for this purpose are; the generation of initial population, tournament selection, crossover with variable position of the cutting points in the parents, artificial mutation operator, deleting of the repetitive genes in same individual, and elitism strategy. Experimental results shows the robust and efficiency of applying the proposed algorithm

CSM-365 - Using schema theory to explore interactions of multiple operators

In the last two years the schema theory for Genetic Programming (GP) has been applied to the problem of understanding the length biases of a variety of crossover and mutation operators on variable length linear structures. In these initial papers, operators were studied in isolation. In practice, however, they are typically used in various combinations, and in this paper we present the first schema theory analysis of the complex interactions of multiple operators. In particular we apply the schema theory to the use of standard subtree crossover, full mutation, and grow mutation (in varying proportions) to variable length linear structures in the one-then-zeros problem. We then show how the results can be used to guide choices about the relative proportion of these operators in order to achieve certain structural goals during a run

Biased dyadic crossover for variable-length multi-objective optimal control problems

This paper presents an enabling technique for social cooperation suitable for variable-length multi-objective direct optimal control problems. Using this approach, individualistic mesh-refinement may be performed across a population of discretised optimal control solutions within a real-coded evolutionary algorithm. Structural homology between individual solutions is inferred via the exploitation of non-uniform dyadic grid structures. Social actions, including genetic crossover, are enabled by identifying nodal intersections between parent vectors in normalised time. Several alternative crossover techniques are discussed, where effectiveness is evaluated based on the likelihood of producing dominating solutions with respect to the current archive. Each technique is demonstrated and compared using a simple numerical test case representing the controlled descent of a Lunar-landing vehicle. Of the examined methods, it is found that a hybrid one/two-point crossover, biased towards higher levels of grid resolution consistently outperforms those based on more traditional, unbiased crossover

Genetic Algorighm Representation Selection Impact on Binary Classification Problems

In this thesis, we explore the impact of problem representation on the ability for the genetic algorithms (GA) to evolve a binary prediction model to predict whether a physical therapist is paid above or below the median amount from Medicare. We explore three different problem representations, the vector GA (VGA), the binary GA (BGA), and the proportional GA (PGA). We find that all three representations can produce models with high accuracy and low loss that are better than Scikit-Learn’s logistic regression model and that all three representations select the same features; however, the PGA representation tends to create lower weights than the VGA and BGA. We also find that mutation rate creates more of a difference in accuracy when comparing the individual with the best fitness (lowest binary cross entropy loss) and the most accurate solution when the mutation rate is higher. We then explore potential of biases in the PGA mapping functions that may encourage the lower values. We find that the PGA has biases on the values they can encode depending on the mapping function; however, since we do not find a bias towards lower values for all tested mapping functions, it is more likely that it is more difficult for the PGA to encode more extreme values given crossover tends to have an averaging effect on the PGA chromosome

Planning And Scheduling For Large-scaledistributed Systems

Many applications require computing resources well beyond those available on any single system. Simulations of atomic and subatomic systems with application to material science, computations related to study of natural sciences, and computer-aided design are examples of applications that can benefit from the resource-rich environment provided by a large collection of autonomous systems interconnected by high-speed networks. To transform such a collection of systems into a user\u27s virtual machine, we have to develop new algorithms for coordination, planning, scheduling, resource discovery, and other functions that can be automated. Then we can develop societal services based upon these algorithms, which hide the complexity of the computing system for users. In this dissertation, we address the problem of planning and scheduling for large-scale distributed systems. We discuss a model of the system, analyze the need for planning, scheduling, and plan switching to cope with a dynamically changing environment, present algorithms for the three functions, report the simulation results to study the performance of the algorithms, and introduce an architecture for an intelligent large-scale distributed system

Behavior Of Variable-length Genetic Algorithms Under Random Selection

In this work, we show how a variable-length genetic algorithm naturally evolves populations whose mean chromosome length grows shorter over time. A reduction in chromosome length occurs when selection is absent from the GA. Specifically, we divide the mating space into five distinct areas and provide a probabilistic and empirical analysis of the ability of matings in each area to produce children whose size is shorter than the parent generation\u27s average size. Diversity of size within a GA\u27s population is shown to be a necessary condition for a reduction in mean chromosome length to take place. We show how a finite variable-length GA under random selection pressure uses 1) diversity of size within the population, 2) over-production of shorter than average individuals, and 3) the imperfect nature of random sampling during selection to naturally reduce the average size of individuals within a population from one generation to the next. In addition to our findings, this work provides GA researchers and practitioners with 1) a number of mathematical tools for analyzing possible size reductions for various matings and 2) new ideas to explore in the area of bloat control

Experimental user interface design toolkit for interaction research (IDTR).

The research reported and discussed in this thesis represents a novel approach to User Interface evaluation and optimisation through cognitive modelling. This is achieved through the development and testing of a toolkit or platform titled Toolkit for Optimisation of Interface System Evolution (TOISE). The research is conducted in two main phases. In phase 1, the Adaptive Control of Thought Rational (ACT-R) cognitive architecture is used to design Simulated Users (SU) models. This allows models of user interaction to be tested on a specific User Interface (UI). In phase 2, an evolutionary algorithm is added and used to evolve and test an optimised solution to User Interface layout based on the original interface design. The thesis presents a technical background, followed by an overview of some applications in their respective fields. The core concepts behind TOISE are introduced through a discussion of the Adaptive Control of Thought “ Rational (ACT-R) architecture with a focus on the ACT-R models that are used to simulate users. The notion of adding a Genetic Algorithm optimiser is introduced and discussed in terms of the feasibility of using simulated users as the basis for automated evaluation to optimise usability. The design and implementation of TOISE is presented and discussed followed by a series of experiments that evaluate the TOISE system. While the research had to address and solve a large number of technical problems the resulting system does demonstrate potential as a platform for automated evaluation and optimisation of user interface layouts. The limitations of the system and the approach are discussed and further work is presented. It is concluded that the research is novel and shows considerable promise in terms of feasibility and potential for optimising layout for enhanced usability

An Analysis of Diversity in Genetic Programming

Genetic programming is a metaheuristic search method that uses a population of variable-length computer programs and a search strategy based on biological evolution. The idea of automatic programming has long been a goal of artificial intelligence, and genetic programming presents an intuitive method for automatically evolving programs. However, this method is not without some potential drawbacks. Search using procedural representations can be complex and inefficient. In addition, variable sized solutions can become unnecessarily large and difficult to interpret. The goal of this thesis is to understand the dynamics of genetic programming that encourages efficient and effective search. Toward this goal, the research focuses on an important property of genetic programming search: the population. The population is related to many key aspects of the genetic programming algorithm. In this programme of research, diversity is used to describe and analyse populations and their effect on search. A series of empirical investigations are carried out to better understand the genetic programming algorithm. The research begins by studying the relationship between diversity and search. The effect of increased population diversity and a metaphor of search are then examined. This is followed by an investigation into the phenomenon of increased solution size and problem difficulty. The research concludes by examining the role of diverse individuals, particularly the ability of diverse individuals to affect the search process and ways of improving the genetic programming algorithm. This thesis makes the following contributions: (1) An analysis shows the complexity of the issues of diversity and the relationship between diversity and fitness, (2) The genetic programming search process is characterised by using the concept of genetic lineages and the sampling of structures and behaviours, (3) A causal model of the varied rates of solution size increase is presented, (4) A new, tunable problem demonstrates the contribution of different population members during search, and (5) An island model is proposed to improve the search by speciating dissimilar individuals into better-suited environments. Currently, genetic programming is applied to a wide range of problems under many varied contexts. From artificial intelligence to operations research, the results presented in this thesis will benefit population-based search methods, methods based on the concepts of evolution and search methods using variable-length representations