Hardware Implementation of a Genetic Algorithm for Motion Path Planning

2016 ◽  
pp. 250-275
Author(s):  
Imbaby I. Mahmoud ◽  
May Salama ◽  
Asmaa Abd El Tawab Abd El Hamid
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Fitness Function ◽  
Random Number Generation ◽  
Motion Path ◽  
Genetic Operators ◽  
Fitness Evaluation ◽  
Evaluation Task ◽  
Evaluation Module ◽  
The Individual

The aim of this chapter is to investigate the hardware (H/W) implementation of Genetic Algorithm (GA) based motion path planning of robot. The potential benefit of using H/W implementation of genetic algorithm is that it allows the use of huge parallelism which is suited to random number generation, crossover, mutation and fitness evaluation. The operation of selection and reproduction are basically problem independent and involve basic string manipulation tasks. The fitness evaluation task, which is problem dependent, however proves a major difficulty in H/W implementation. Another difficulty comes from that designs can only be used for the individual problem their fitness function represents. Therefore, in this work the genetic operators are implemented in H/W, while the fitness evaluation module is implemented in software (S/W). This allows a mixed hardware/software approach to address both generality and acceleration. Moreover, a simple H/W implementation for fitness evaluation of robot motion path planning problem is discussed.

scholarly journals Application of the genetic algorithm to the task of compiling the curriculum

2018 ◽  
Author(s):  
V. A. Turchina ◽  
D. O. Tanasienko
Keyword(s):  
Higher Education ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Necessary Conditions ◽  
Fitness Function ◽  
Educational Process ◽  
Heuristic Methods ◽  
Educational Groups ◽  
The Individual ◽  
Negative Property

One of the main tasks in organizing the educational process in higher education is the drawing up of a schedule of classes. It reflects the weekly student and faculty load. At the same time, when compiling, there are a number of necessary conditions and a number of desirable. The paper considers seven required and four desirable conditions. In this paper, one of the well-known approaches that can be used in drawing up a curriculum is consid-ered. The proposed scheme of the genetic algorithm, the result of which is to obtain an approximate solution to the problem of scheduling with the need to further improve it by other heuristic methods. To solve the problem, an island model of the genetic algorithm was selected and its advantages were considered. In the paper, the author's own structure of the individual, which includes chromosomes in the form of educational groups and genes as a lesson at a certain time, is presented and justified. The author presents his own implementations of the genetic algorithms. During the work, many variants of operators were tested, but they were rejected due to their inefficiency. The biggest problem was to maintain the consistency of information encoded in chromosomes. Also, two post-steps were added: to try to reduce the number of teacher conflict conflicts and to normalize the schedule - to remove windows from the schedule. The fitness function is calculated according to the following principles: if some desired or desired property is present in the individual, then a certain number is deducted from the individual's assessment, if there is a negative property, then a certain number is added to the assessment. Each criterion has its weight, so the size of the fine or rewards may be different. In this work, fines were charged for non-fulfillment of mandatory conditions, and rewards for fulfilling the desired

Global Path Planning for Full-Area Coverage Robotic Systems by Employing an Active Genetic Algorithm

2011 ◽  
Vol 328-330 ◽  
pp. 1881-1886
Author(s):  
Cen Zeng ◽  
Qiang Zhang ◽  
Xiao Peng Wei
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
High Performance ◽  
Search Space ◽  
Area Coverage ◽  
Robotic Systems ◽  
Genetic Operators ◽  
A Algorithm ◽  
Coverage Path Planning ◽  
Search Spaces

Genetic algorithm (GA), a kind of global and probabilistic optimization algorithms with high performance, have been paid broad attentions by researchers world wide and plentiful achievements have been made.This paper presents a algorithm to develop the path planning into a given search space using GA in the order of full-area coverage and the obstacle avoiding automatically. Specific genetic operators (such as selection, crossover, mutation) are introduced, and especially the handling of exceptional situations is described in detail. After that, an active genetic algorithm is introduced which allows to overcome the drawbacks of the earlier version of Full-area coverage path planning algorithms.The comparison between some of the well-known algorithms and genetic algorithm is demonstrated in this paper. our path-planning genetic algorithm yields the best performance on the flexibility and the coverage. This meets the needs of polygon obstacles. For full-area coverage path-planning, a genotype that is able to address the more complicated search spaces.

High-dimensional Unbalanced Binary Classification by Genetic Programming with Multi-criterion Fitness Evaluation and Selection

2021 ◽  
pp. 1-26
Author(s):  
Wenbin Pei ◽  
Bing Xue ◽  
Lin Shang ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Fitness Function ◽  
Binary Classification ◽  
Class Imbalance ◽  
Area Under The Curve ◽  
High Dimensional ◽  
Genetic Operators ◽  
Minority Class ◽  
Fitness Evaluation ◽  
Unbalanced Classification

Abstract High-dimensional unbalanced classification is challenging because of the joint effects of high dimensionality and class imbalance. Genetic programming (GP) has the potential benefits for use in high-dimensional classification due to its built-in capability to select informative features. However, once data is not evenly distributed, GP tends to develop biased classifiers which achieve a high accuracy on the majority class but a low accuracy on the minority class. Unfortunately, the minority class is often at least as important as the majority class. It is of importance to investigate how GP can be effectively utilized for high-dimensional unbalanced classification. In this paper, to address the performance bias issue of GP, a new two-criterion fitness function is developed, which considers two criteria, i.e. the approximation of area under the curve (AUC) and the classification clarity (i.e. how well a program can separate two classes). The obtained values on the two criteria are combined in pairs, instead of summing them together. Furthermore, this paper designs a three-criterion tournament selection to effectively identify and select good programs to be used by genetic operators for generating better offspring during the evolutionary learning process. The experimental results show that the proposed method achieves better classification performance than other compared methods.

Evolved Synthesis of Digital Circuits

2011 ◽  
pp. 609-617 ◽  
Author(s):  
Laurentiu Ionescu ◽  
Alin Mazare ◽  
Gheorghe Serban ◽  
Emil Sofron
Keyword(s):  
Target Function ◽  
Living Organism ◽  
Digital Circuit ◽  
Evolvable Hardware ◽  
Genetic Operators ◽  
Rules And Principles ◽  
Evaluation Module ◽  
The Individual ◽  
High Level ◽  
Crossover And Mutation

Traditionally physical systems have been designed by engineers using complex collections of rules and principles. The design process is top-down in nature and begins with a precise specification. This contrasts very strongly with the mechanisms which have produced the extraordinary diversity and sophistication of living creatures. In this case the ‘‘designs’’ are evolved by a process of natural selection. The design starts as a set of instructions encoded in the DNA whose coding regions are first transcribed into RNA in the cell nucleus and then later translated into proteins in the cell cytoplasm. The DNA carries the instructions for building molecules using sequences of amino acids. Eventually after a number of extraordinarily complex and subtle biochemical reactions an entire living organism is created. The survivability of the organism can be seen as a process of assembling a larger system from a number of component parts and then testing the organism in the environment in which it finds itself (Miller, 2000). The main target of the evolvable hardware is to build a digital circuit using bio inspired methods like genetic algorithms. Here the potential solutions are coded like configuration vectors which command interconnection between logical cells inside the reconfigurable circuit. All configuration vectors represent the genotype and one single configuration vector is the individual with its own characteristics (like chromosome). The individuals are generated by genetic operators like crossover or mutation. One individual give one solution circuit which is tested in evaluation module. The circuit obtained from the individual consist the phenotype. The circuit behavior is compared with target functions, which we desire to implement. The result is fitness: if the circuit approximates the behavior of the target function, we have a good fitness for the individual which generate the circuit. Then each individual whit its fitness gets into selection module where the future parents in crossover and mutation are decided. Finally we have a circuit solution which implements the target function. We have an evolved synthesis of digital circuit – a method like assemble and test. This method can be useful because explore the design space beyond the limits imposed by traditional design methods. Two research directions are developed in evolvable hardware. In extrinsic evolvable hardware the individuals are obtained from software implementation on computer and phenotype consist in high level abstract circuits like SPICE object files or FPGA configuration files (.bit). The intrinsic evolution, on the other hand, supposes that entire evolution process is inside one or more chips (FPGA): the hardware implementation of evolved hardware. The challenge is to design an intrinsic evolution because can be used for applications like robots control system. But this involves implementation of the software based algorithms in hardware modules.

scholarly journals Determining optimal combination of roller compacted concrete pavement mixture containing recycled asphalt pavement and crumb rubber using hybrid artificial neural network–genetic algorithm method considering energy absorbency approach

2017 ◽  
Vol 44 (11) ◽  
pp. 945-955 ◽  
Author(s):  
Mansour Fakhri ◽  
Ershad Amoosoltani ◽  
Mona Farhani ◽  
Amin Ahmadi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Neural Network Model ◽  
Fitness Function ◽  
Crumb Rubber ◽  
Concrete Pavement ◽  
Genetic Operators ◽  
Efficiency Criterion ◽  
Roller Compacted Concrete

The present study investigates the effectiveness of evolutionary algorithms such as genetic algorithm (GA) evolved neural network in estimating roller compacted concrete pavement (RCCP) characteristics including flexural and compressive strength of RCC and also energy absorbency of mixes with different compositions. A real coded GA was implemented as training algorithm of feed forward neural network to simulate the models. The genetic operators were carefully selected to optimize the neural network, avoiding premature convergence and permutation problems. To evaluate the performance of the genetic algorithm neural network model, Nash-Sutcliffe efficiency criterion was employed and also utilized as fitness function for genetic algorithm which is a different approach for fitting in this area. The results showed that the GA-based neural network model gives a superior modeling. The well-trained neural network can be used as a useful tool for modeling RCC specifications.

scholarly journals A Novel Fast Decision-making Method Based on Integrated Intelligent Technologies for Wind Disaster Management of Energy Manufacture

CONVERTER ◽  
2021 ◽  
pp. 169-190
Author(s):  
Baishang Zhang, Et al.
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Evidence Theory ◽  
Nonlinear Problems ◽  
Case Based Reasoning ◽  
Genetic Operators ◽  
Electricity Grid ◽  
Fast Decision

Energy manufacture is very important to all of industries. Typhoons hit the power grid in China's southeast coastal areas frequently for the past few years, seriously affecting the industries’ operation. Therefore, making-decision of wind damage management for nation's electricity grid in real time is an urgent subject to be studied. The traditional decision making method is easy to be implemented, but is not proper for dealing with nonlinear problems in complex systems. The purpose of this article is to design a fast decision making framework for accomplishing fast decision making by making combination Case-Based Reasoning (CBR) with Rule-Based Reasoning (RBR), Genetic Algorithm (GA), which is called fast decision making method based on integrated intelligent technologies (FDMMBIIT). Compared with traditional methods, FDMMBIIT completes case adaptation with BPNN after extending case base. To make the decision-making more accurate, this article updated the multi-object genetic algorithm (MOGA) with adaptive genetic operators and a selection method by using the fitness function. Likewise, BPNN is improved with adaptive simulated annealing algorithm (ASAA), which is named as BPNNASAA. More important, this paper expands the frame theory by integrating it to the D/S evidence theory, exploring a novel solution to representing cases with incomplete information. The case of Guangdong demonstrates FDMMBIIT achieves better decision-making performance for storm disaster emergency management.

scholarly journals ESTIMATION OF SEISMIC WAVES PARAMETERS WITH APPLICATION OF A GENETIC ALGORITHM

2018 ◽  
pp. 51-58
Author(s):  
R. R. Sultangaleev ◽  
V. N. Troyan
Keyword(s):  
Genetic Algorithm ◽  
Seismic Waves ◽  
Fitness Function ◽  
Random Number Generator ◽  
Synthetic Data ◽  
Threshold Value ◽  
Velocity Estimation ◽  
Model Parameters ◽  
Basic Step ◽  
The Individual

A Genetic algorithm (GA) is a very important method for the solution of non-linear problems. The basic steps in GA are coding, selection, crossover, mutation and choice. Coding is a way of representing data  in binary notation. The algorithm must determine the fitness of the individual models. This means that  the binary information is decoded into the physical model parameters and the forward problem is solved. The resulting synthetic data is estimated, then compared with the actual observed data using the  specific fitness criteria. The selection of pairs of the individual models for the reproduction is based on  their fitness values. Models with the higher fitness values are more likely to get the selection than models with low  fitness values. A crossover caused the exchange of some information between the paired models thereby  generating new models. The mutation is a random change of binary state. The condition of the procedure of mutation: if a value obtained by a random number generator is less than a certain threshold value, the  mutation procedure is performed. The last basic step in GA is choice. We choose from each pairs a model,  which has the less fitness function. Then we produce the procedures: the crossover, the mutation and the  choice. This procedure is continued until we obtain the optimal model. We have used the GA for the  estimation of the velocity for the gradient layer. The synthetic seismogram was calculated by the finite- difference method. The obtained results showed a high effectiveness of GA for the seismic waves velocity estimation.

Automatic Generation of Design Scheme Based on Improved Segmentation Genetic Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 1655-1660
Author(s):  
Ji Wen Chen ◽  
Bo Huang ◽  
Bo Pang ◽  
Li Li
Keyword(s):  
Genetic Algorithm ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Information Seeking ◽  
Structural Information ◽  
Fitness Function ◽  
Automatic Generation ◽  
Genetic Operators ◽  
Structure Information ◽  
Design Scheme

Research on composition and quantitative representation of the function unit structural information, seeking the combination process effectively, is the key technology to generate product design schemes. Full life cycle assessment properties of structure is introduced into expression of design scheme. The gene model of life cycle assessment properties of structure is established, and the variable length coding is converted to equal length coding to realize the quantitative representation of structure information. The fitness function is established for life cycle assessment of structure with Analytic Hierarchy Process. The improved segmentation genetic algorithm is studied. The gene sequence of design scheme is segmented. Genetic operators such as across and mutate is designed for structure information the segmented gene fragment. Life cycle assessment gene of structure as attribute does not participate in the genetic operation. Design schemes automatic generation is achieved based on improved segmented genetic operators, reflecting life cycle assessment properties of design schemes.

scholarly journals Solving the Energy Efficient Coverage Problem in Wireless Sensor Networks: A Distributed Genetic Algorithm Approach with Hierarchical Fitness Evaluation

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3526 ◽  
Author(s):  
Zi-Jia Wang ◽  
Zhi-Hui Zhan ◽  
Jun Zhang
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Fitness Function ◽  
Computational Cost ◽  
Wireless Sensor ◽  
Fitness Evaluation ◽  
Disjoint Sets ◽  
Distributed Genetic Algorithm

This paper proposed a distributed genetic algorithm (DGA) to solve the energy efficient coverage (EEC) problem in the wireless sensor networks (WSN). Due to the fact that the EEC problem is Non-deterministic Polynomial-Complete (NPC) and time-consuming, it is wise to use a nature-inspired meta-heuristic DGA approach to tackle this problem. The novelties and advantages in designing our approach and in modeling the EEC problems are as the following two aspects. Firstly, in the algorithm design, we realized DGA in the multi-processor distributed environment, where a set of processors run distributed to evaluate the fitness values in parallel to reduce the computational cost. Secondly, when we evaluate a chromosome, different from the traditional model of EEC problem in WSN that only calculates the number of disjoint sets, we proposed a hierarchical fitness evaluation and constructed a two-level fitness function to count the number of disjoint sets and the coverage performance of all the disjoint sets. Therefore, not only do we have the innovations in algorithm, but also have the contributions on the model of EEC problem in WSN. The experimental results show that our proposed DGA performs better than other state-of-the-art approaches in maximizing the number of disjoin sets.

scholarly journals A Genetic Algorithm Based Minimal Maximal Load Tree Routing Algorithm in Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Zhi Huang
Keyword(s):  
Genetic Algorithm ◽  
Network Lifetime ◽  
Fitness Function ◽  
Routing Algorithm ◽  
Optimal Routing ◽  
Energy Efficient Routing ◽  
Maximal Load ◽  
The Individual ◽  
Routing Tree ◽  
Static Routing

The key issue of static routing algorithms is how to construct an energy efficient routing tree that is utilized during the whole network duration in order to extend network lifetime. In this paper, we have illuminated that, in applications that define network lifetime as the time when the first sensor dies, the optimal routing tree should be the routing tree with minimal maximal load of all sensors and named such trees the Minimal Maximal Load Tree (MMLT). Since the procedure of constructing a routing tree is complex and the number of possible routing trees in a network is very huge, we have proposed a genetic algorithm (GA) based algorithm to obtain approximate Minimal Maximal Load Tree (MMLT). Each individual corresponds to a routing tree, and the fitness function is defined as the maximal load of all sensors in accordance with the routing tree that the individual corresponds to. Thus, approximate MMLT is obtained and network lifetime is extended. Simulation results show that our proposed algorithm notably extends network lifetime.

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