Genetic programming tuned fuzzy controlled traffic light system

The 0/1 knapsack problem is one of the most exhaustively studied NP-hard combinatorial optimization problems. Many different approaches have been taken to obtain an approximate solution to the problem in polynomial time. Here we consider the biobjective 0/1 knapsack problem. The contribution of this paper is to show that a genetic programming system can evolve a set of heuristics that can give solutions on the Pareto front for multiobjective combinatorial problems. The genetic programming (GP) system outlined here evolves a heuristic which decides whether or not to add an item to the knapsack in such a way that the final solution is one of the Pareto optimal solutions. Moreover, the Pareto front obtained from the GP system is comparable to the front obtained from other human-designed heuristics. We discuss the issue of the diversity of the obtained Pareto front and the application of strongly-typed GP as a means of obtaining better diversity.

Genetic programming (GP) is applied to the design of fuzzy logic controllers (FLCs) for mobile robot path tracking. GP is applied to automatic discovery of full knowledge bases for use in fuzzy logic control applications. An extension to a rule learning GP system is presented that achieves this objective. In addition, GP is employed to handle selection of fuzzy set intersection operators (t-norms). The new GP system is applied to design a mobile robot path tracking controller and performance is shown to be comparable to that of a manually designed controller. GP was successfully applied to discover FLCs capable of steering a mobile robot to track straight-line paths in the plane. Instances of simultaneous evolution of membership functions and rules showed that GP was capable of evolving a FLC that demonstrated satisfactory responsiveness to various initial conditions while utilizing minimal human interface.

In this work, genetic programming systems are viewed as cooperative processes: individuals in the population cooperate to evolve a global solution. There are two basic forms of cooperation for distributed problem solving: task-sharing and result-sharing. We introduce two models that enable cooperation in genetic programming systems. The first model is based on task-sharing and the second one is based on result-sharing. We examine the effects of both forms of cooperation on the performance of genetic programming systems solving the insect locomotion problem. This article demostrates that cooperative genetic programming can be used to evolve several commonly observed gaits in insects.

Programming languages provide a variety of mechanisms to associate names with values, and these mechanisms play a central role in programming practice. For example, they allow multiple references to the same storage location or function in different parts of a complex program. By contrast, the representations used in current genetic programming systems provide few if any naming mechanisms, and it is therefore generally not possible for evolved programs to use names in sophisticated ways. In this chapter we describe a new approach to names in genetic programming that is based on Holland’s concept of tags. We demonstrate the use of tag-based names, we describe some of the ways in which they may help to extend the power and reach of genetic programming systems, and we look at the ways that tag-based names are actually used in an evolved program that solves a robot navigation problem.Keywordsgenetic programmingmodularitynamestagsstack-based genetic programmingPush programming languagePushGP

In this paper, a hierarchical fuzzy logic traffic controller is constructed for a real intersection of fourteen vehicle lanes and two pedestrian crossings controlled by signals with seven light phases. The hierarchical fuzzy controller has seven inputs as queue lengths of the seven light phases, and one output as green time of the selected phase. In the hierarchical fuzzy controller, there are six layers of fuzzy sub-controllers with two inputs and one output. The sub-controllers in the first five layers have identical structure that has two inputs of queue lengths and one output of combined queue length employed as one input of next layer. The sub-controller in the last layer has two inputs, combined queue length obtained from the fifth layer and queue length of the selected phase, and one output as green time of the selected phase. Using the developed fuzzy controller, the best fuzzy rule base is obtained based on real traffic data of the intersection by employing evolutionary algorithm. The performance of this controller is simulated and compared to that of a controller that is currently employing in the intersection. The results show that the developed fuzzy controller shortens more than 38% of the vehicle waiting time.

Gaussian-based edge detectors have been developed for many years, but there are still problems with how to set scales for Gaussian filters and how to combine Gaussian filters. In order to address both problems, a Genetic Programming (GP) system is proposed to automatically choose scales for Gaussian filters and automatically combine Gaussian filters. In this study, the GP system is utilised to construct rotation invariant Gaussian-based edge detectors based on a benchmark image dataset. The experimental results show that the GP evolved Gaussian-based edge detectors are better than the Gaussian gradient and rotation invariant surround suppression to extract edge features.

Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations

The escalating global population and increased vehicle usage have worsened traffic congestion in metropolitan areas, a significant urban challenge. Addressing this, adaptive traffic light control methods, especially at intersections, are being developed to improve traffic flow and reduce waiting times. This study significantly contributes to this field by implementing Fuzzy Logic in intelligent traffic light systems, focusing on Ankara's Polatlı Refik Cesur intersection. Using the SUMO simulation platform and Python programming, it analyzed waiting times and queue lengths. The initial phase used queue length for each intersection arm as an input. Fuzzy logic rules then determined the output, prioritizing street or phase order for optimal flow. The study further proposed an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based control plan. ANFIS merges neural network capabilities with fuzzy logic, using waiting time and queue length as inputs to regulate the green light duration. Compared to existing traffic systems, this model showed a substantial improvement. It achieved a 36.5% reduction in waiting times, underlining the efficiency of the Fuzzy Logic-based method. This approach not only enhances traffic management but also contributes significantly to the literature on intelligent traffic light control systems. By addressing key urban traffic issues, the study paves the way for future advancements in traffic management technologies. The findings highlight the potential of combining advanced computational methods, like ANFIS, with traditional traffic control techniques to optimize urban traffic flow, offering a blueprint for similar challenges in other metropolitan areas.

This paper developed a fuzzy logic controller with a genetic algorithm control system for sensor based pipeline robot navigation in pipe environment. The fuzzy controller creates initial membership functions and fuzzy logic rules. the genetic algorithm, which automatically designs a fuzzy logic controller by modifying the position of controller membership functions and the commands given to the pipeline robot, optimizes the fuzzy controller. This GA-fuzzy system is used to control the mobile pipeline robot, which is equipped with an array of ultrasonic sensors to acquire the distance between the pipeline robot and obstacle. On the basis of this system, a strategy for combining local behavior control with global behavior control planning was proposed in this paper. Finally a pipeline robot runs in simulation environment. The experimental results show that appropriate control mechanisms of the fuzzy controller are obtained by evolution. The controller has evolved well enough to smoothly drive the pipeline robot in different pipe environment.

Fuzzy logic is a topic traditionally taught in artificial intelligence, machine learning, and robotics courses. Students receive the necessary mathematical and theoretical foundation in lecture format. The final learning experience may require that students create and code their own fuzzy logic application that solves a real world problem. This can be an issue when the target is a bioengineering course that introduces classical control theory, fuzzy logic, neural networks, genetic algorithms and genetic programming through the use of a low cost robot, personal digital assistant (PDA) handheld computer, and custom PDA software. In this course, the concepts and theories discussed in lecture are reinforced and extended in a corresponding laboratory through the use of wireless robots and PDAs. Fuzzy logic libraries and software modules for laptops and desktop computers are readily available, however, when it comes to handheld computers no such libraries exist. Students are able to spend more time experimenting with different fuzzy logic controllers when a custom fuzzy logic library and PDA graphical user interface are utilized. In this paper we introduce and discuss a unique low cost wireless robot, a custom fuzzy logic library, a custom fuzzy logic GUI for the PDA, and the implementation results for the fuzzy logic section in a newly created bioengineering course. Diagnostic and summative assessment in the form of a pre-test and post-test was administered for each section of the course, however, only the results for the fuzzy logic section will be provided.

Document classification tasks generally have sparse and high dimensional features. It is important to effectively extract features. In document classification tasks, there are some similarities existing in different categories or different datasets. It is possible that one document classification task does not have labelled training data. In order to obtain effective classifiers on this specific task, this paper proposes a Genetic Programming (GP) system using transductive transfer learning. The proposed GP system automatically extracts features from different source domains, and these GP extracted features are combined to form new classifiers being directly applied to a target domain. From experimental results, the proposed transductive transfer learning GP system can evolve features from source domains to effectively apply to target domains which are similar to the source domains.

The central point in designing a fuzzy system for modeling and control is the generation of a set of efficient and effective fuzzy rules. Generally, fuzzy rules can be generated either from heuristics or from experimental data. Heuristic fuzzy rule generation, as discussed in Chapter 1, consists of the following main steps: Determination of the structure of the fuzzy systems. This includes mainly the selection of the inputs and outputs. Usually, a multi-input multi-output fuzzy systems can be decomposed into a number of multi-input single-output fuzzy systems. Definition of linguistic terms and their fuzzy membership function for each fuzzy variables. If a Mamdani fuzzy system is designed, each input and output can be seen as a linguistic variable. Therefore, it is necessary to define the linguistic terms (e.g., Small, Medium, Large etc.) and their fuzzy membership function. Details about the definition of a linguistic variable is presented in Section 1.2.1. Determination of fuzzy inference method. For example, for fuzzy mapping rules, the fuzzy Cartesian product can be used to define the fuzzy relation. Refer to Section 1.2.2. Defuzzification. If the Mamdani type fuzzy rules are used, the fuzzy output derived from the fuzzy rule base should be defuzzified. Refer also to Section 1.2.2 for different defuzzification methods. If the Takagi-Sugeno fuzzy rules are used, the defuzzification is included in the fuzzy inference and a crisp output is obtained directly.

We examine the performance of compact fuzzy rule-based classification systems that consist of a small number of simple fuzzy rules with high comprehensibility. Those fuzzy systems are designed in a heuristic manner using rule selection criteria. We first describe fuzzy rule-based classification. Next we describe heuristic rule selection criteria using the terminology in data mining: confidence and support. A small number of fuzzy rules are extracted from numerical data based on each rule selection criterion. Then we examine the classification performance of extracted fuzzy rules through computational experiments on a number of benchmark data sets from the UCI ML Repository. Our results should be viewed as the lowest benchmark performance of fuzzy rule-based classification systems because fuzzy rules are extracted using a simple heuristic method with no optimization or tuning procedures. Nevertheless our results on some data sets are comparable to reported results by the C4.5 algorithm in the literature.

Review of <i>Environmental and Hydrological Systems Modeling</i> by A. W. JayawardenaCRC Press Taylor &amp; Francis Group, Boca Raton, FL 33487; 2014; ISBN 978-0-415-46532-8; 516 pp.; $99.95.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper presents a novel fuzzy model and a fuzzy logic controller for an isolated signalized intersection. The controller controls the traffic light timings and phase sequence to ensure smooth flow of traffic with minimal waiting time and length of queue. Usually fuzzy traffic controllers are optimized to maximize traffic flows/minimize traffic waiting time under typical traffic conditions. Consequentially, these are not the optimal traffic controllers under exceptional traffic cases such as roadblocks and road accidents. We apply state-space equations to formulate the average waiting time vehicles in traffic network at fixed time control. also, We propose a novel fuzzy model and new fuzzy traffic controller that can optimally control traffic flows under both normal and exceptional traffic conditions. Results show that the performance of the proposed traffic controller at novel fuzzy model is better that of conventional fuzzy traffic controllers under normal and abnormal traffic conditions. </para>