CHAPTER 5 ENERGY MANAGEMENT USING FUZZY GENETIC APPROACH IN WSN

Size: px
Start display at page:

Download "CHAPTER 5 ENERGY MANAGEMENT USING FUZZY GENETIC APPROACH IN WSN"

Transcription

1 97 CHAPTER 5 ENERGY MANAGEMENT USING FUZZY GENETIC APPROACH IN WSN 5.1 INTRODUCTION Fuzzy systems have been applied to the area of routing in ad hoc networks, aiming to obtain more adaptive and flexible models over existing models. While the main objective in the design of fuzzy rule-based systems has been the performance maximization, their comprehensibility has also been taken into. The comprehensibility of fuzzy rule-based systems is related to various factors (Ishibuchi & Yamamoto 2004): (i) Comprehensibility of fuzzy partitions (e.g., linguistic interpretability of each fuzzy set, separation of neighboring fuzzy sets, the number of fuzzy sets for each variable). (ii) Simplicity of fuzzy rule-based systems (e.g., the number of input variables, the number of fuzzy if-then rules). (iii) Simplicity of fuzzy if-then rules (e.g., type of fuzzy if-then rules, the number of antecedent conditions in each fuzzy if-then rule). (iv) Simplicity of fuzzy reasoning (e.g., selection of a single winner rule, voting by multiple rules). Rule selection is a direct approach to the design of fuzzy rule-based systems. As seen in the last chapter, the number of the fuzzy rules got is high. In the proposed approach, Genetic Algorithm (GA) is utilized to choose a

2 98 small number of significant fuzzy rules and removal of unnecessary fuzzy rules. Though not specifically designed for learning, but rather GAs were used as global search algorithms, they offer advantages for machine learning. Methodologies for machine learning search for a good model in the space of all possible models. In this sense, their flexibility can be used with different representations. Different levels of complexity are covered in genetic learning processes according to the structural changes formed by the algorithm, the simple case being parameter optimization to the complex case being to learn rule set of a rule-based system, through the coding approach and the competition or cooperation between chromosomes. When focusing on learning rules and considering a rule based system, the different genetic learning methods follow two methods in order to encode rules in a population of individuals (Herrera 2008): The Chromosome = Set of rules (the Pittsburgh approach) - here each individual represents a rule set (Smith 1980). A chromosome evolves a complete Rule Base (RB) competing among them along the evolutionary process. Proposals that follow this approach are GASSITS and GABIL (Bacardit et al 2003; Bacardit et al 2007). The Chromosome = Rule approach -here each individual codifies a single rule, and combining several individuals in a population the whole rule set is provided (rule cooperation) or through different evolutionary runs (rule competition). In the Chromosome = Rule approach, the generic proposals are: Michigan approach -here each individual codifies an association rule. They are called learning classifier systems

3 99 (Holland 1978). In a given environment, they are rule-based, message passing systems that use reinforcement learning and GA to learn rules that helps their performance. GA is used for detecting new rules and replacing the bad ones via the competition between the chromosomes in the evolutionary process. A study on the topic can be found in Kovacs (2004). The IRL (Iterative Rule Learning) approach -here each chromosome represents a rule. The chromosomes compete in each GA run, choosing best rule per run. Global solution is designed by the best rules got when the algorithm is run multiple times. SIA (Venturini 1993) is a scheme that follows this approach. 5.2 METHODOLOGY Fuzzy rule-based systems have been successfully applied in the previous chapter and shown to improve the performance of existing routing protocols. The comprehensibility off fuzzy rule-based systems is related to various factors: (i) Comprehensibility of fuzzy partitions (e.g., linguistic interpretability of each fuzzy set, separation of neighboring fuzzy sets, the number of fuzzy sets for each variable). (ii) Simplicity off fuzzy rule-based systems (e.g., the number of input variables, the number off fuzzy if-then rules). (iii) Simplicity of fuzzy if-then rules (e.g., type of fuzzy if-then rules, the number of antecedent conditions in each fuzzy if-then rule).

4 100 (iv) Simplicity off fuzzy reasoning (e.g., selection of a single winner rule, voting by multiple rules). In this work a small number of simple fuzzy if-then rules is selected using Genetic Algorithm for designing a comprehensible fuzzy rule-based system for routing problem with many continuous attributes. Among the above four issues, the second and third ones are mainly discussed in this work. The proposed methodologies are explained in detail in the following sections. The flowchart in figure 5.1 explains the methodologies.

5 101 Figure 5.1 Flowchart for proposed methodology Genetic Algorithm Genetic Algorithms (GA) are evolution inspired computational models which encode a potential solution for a specific problem on chromosome-like data structure applying recombination operators to structures

6 102 to preserve critical information. GA is viewed as function optimizers, though they can be applied to a wide problem range (Mathew 1996). 1. Initialization : The initial candidate solutions population is randomly generated across search space. But domain-specific knowledge/other information are easily incorporated. 2. Evaluations : Once population is initialized or offspring population created, candidate solutions fitness values are evaluated. 3. Selection : Selection allocates copies of solutions with higher fitness values imposing survival-of-the-fittest mechanism on candidate solutions. 4. Recombination : Recombination integrates parts of two or more parental solutions for the creation of new and possibly improved solutions (offspring). There are many ways to accomplish this (discussed in next section), with competent performance depending on correctly designed recombination mechanism. 5. Mutation : While recombination operates on two or more parental chromosomes, mutation modifies a solution locally and randomly. There are many mutation variations usually involving one or more changes to an individual s trait/traits. Mutation performs a random walk near a candidate solution. 6. Replacement : Offspring created by selection, recombination, and mutation substitute original parental population. 7. Repeat steps : 2 6 till a terminating condition is met (Sastry et al 2005).

7 103 GA Operators : The simplest form of genetic algorithm involves three types of operators: selection, crossover (single point) and mutation. Selection : This operator selects chromosomes in the population for reproduction. The fitter the chromosome, the more times it is likely to be selected to reproduce. (5.1) Where (i) and f (i) are the probability of selection and fitness value for the ith chromosome respectively (Melanie 1999). Crossover : This operator chooses a locus randomly exchanging subsequences before and after locus between two chromosomes to create two offspring. A crossover probability is predetermined before algorithm starts, governing whether a parent pair is crossed-over or reproduced. Reproduction results in offspring being exactly equal to parent pair. Crossover converts parent pair to binary notation swapping bits after random selected crossover point to form offspring pair. Mutation : Mutations are global searches. A mutation probability is predetermined prior to the algorithm starting, being applied to offspring chromosome s individual bit to determine whether it is to be inverted (Mishra & Patnaik 2009). The following Figure5.2 shows the Flowchart for genetic algorithm.

8 104 Figure 5.2 Flowchart of genetic algorithm A genetic algorithm-based approach is proposed for selecting a small number of fuzzy if-then rules from a large number of candidate rules. A table as a genotype with alleles that are fuzzy set indicators over the output domain is considered. The phenotype is produced by the behavior produced by the fuzzi erations Hybrid fuzzy-ga based approach The steps of the hybrid fuzzy-ga algorithm are: 1. Read the input as the metric values 2. Find the nearest match with Example data

9 Calculate the Output of the fuzzy Inference System corresponding to the Input set 4. Treat FIS value and the Nearest Match Value as Chromosome and convert the values into Binary after multiplying the values with Perform the crossover of the Values at a Particular Point 6. Compare the results 5.3 EXPERIMENTAL SETUP Simulations are conducted by using varying number of sensor nodes deployed in 4 square km, with a maximum of 5 hops from the sink node. The number of nodes was varied 50 to 300. The transmission power of a node is taken as 0.005w and 11 Mbps is taken as maximum bandwidth. Number of packets to be transferred, residual energy and number of hops to the sink node are taken as input variables and energy can be sent has taken as an output variable. 5.4 RESULTS AND DISCUSSION Figure 5.3 to 5.8 shows the comparison of performance of M- algorithm, Fuzzy based routing and proposed fuzzy genetic algorithm. Table 5.1 Average Packet Delivery Ratio Number of nodes Fuzzy M Fuzzy GA M Fuzzy Fuzzy GA

10 106 Average Packet Delivery Ratio Number of Nodes Fuzzy M Fuzzy GA M Fuzzy Fuzzy GA Figure 5.3 Average Packet Delivery Ratio Experimental results conducted for Energy Management using Fuzzy Genetic Approach in WSN showed in Table 5.1 and Figure 5.3 enlighten that the Packet Delivery Ratio increases when compared to M- and Fuzzy. When number of nodes is 50 PDR increases by 1.17% for Fuzzy GA when compared to Fuzzy and by 3.93% for M-, when number of nodes is 100 by 2.18% for Fuzzy and by 5.19% for M-, when number of nodes is 150 by 2.28% for Fuzzy and by 5.97% for M-, when number of nodes is 200 by 2.18% for Fuzzy and by 5.87% for M-, when number of nodes is 250 by 0.95% for Fuzzy and by 3.43% for M-, and when number of nodes is 300 by 2.18% Fuzzy and by 13.47% for M-.

11 107 Table 5.2 Average End To End Delay in second Number of nodes Fuzzy M Fuzzy GA M Fuzzy Fuzzy GA Average End To End Delay (sec) Number of Nodes Fuzzy M Fuzzy GA M Fuzzy Fuzzy GA Figure 5.4 Average End-To-End Delay Experimental results conducted for Energy Management using Fuzzy Genetic Approach in WSN showed in Table 5.2 and Figure 5.4 enlighten that the End to End Delay decreases when compared to M- and Fuzzy. When number of nodes is 50 End to End Delay decreases by 10.97% for Fuzzy GA when compared to Fuzzy and by 14.89% for M-

12 108, when number of nodes is 100 by 11.83% for Fuzzy and by 20.63% for M-, when number of nodes is 150 by 3.01% for Fuzzy and by 52.78% for M-, when number of nodes is 200 by 8.25% for Fuzzy and by 68.11% for M-, when number of nodes is 250 by 6.5% for Fuzzy and by 38.27% for M-, and when number of nodes is 300 by 6.09% Fuzzy and by 85.27% for M-. Table 5.3 Number of clusters formed Number of nodes Fuzzy M Fuzzy GA M Number of Clusters formed Number of nodes Fuzzy Fuzzy GA Figure 5.5 Number of cluster formed

13 109 Experimental results conducted for Energy Management using Fuzzy Genetic Approach in WSN showed in Table 5.3 and Figure 5.5 enlighten that the Number of clusters formed decreases when compared to M- and Fuzzy. When number of nodes is 50 and 100 the Number of clusters formed is same for Fuzzy and Fuzzy GA. When number of nodes is 50 the Number of clusters formed decreases by 12.50% for Fuzzy GA when compared to M-, when number of nodes is 100 by 21.42% for M-, when number of nodes is 150 by 6.66% for Fuzzy and by 22.22% for M-, when number of nodes is 200 by 4.76% for Fuzzy and by 23.07% for M-, when number of nodes is 250 by 3.70% for Fuzzy and by 18.75% for M-, and when number of nodes is 300 by 6.45% Fuzzy and by 19.44% for M-. Table 5.4 Average number of hops to sink Number of nodes Fuzzy M Fuzzy GA M Fuzzy Fuzzy GA

14 110 Average Number of Hops to Sink Number of Nodes Fuzzy M Fuzzy GA M Fuzzy Fuzzy GA Figure 5.6 Average number of hops to sink Experimental results conducted for Energy Management using Fuzzy Genetic Approach in WSN showed in Table 5.4 and Figure 5.6 enlighten that the Average no of hops to sink decreases when compared to M- and Fuzzy for higher number of nodes. When number of nodes is 50 and 100, the no of hops to sink, increases for Fuzzy and Fuzzy GA when compared to M-Leach. When number of nodes is 50 the no of hops to sink increases by 7.14% for Fuzzy GA when compared to Fuzzy and by 14.28% to M-, when number of nodes is 100 by 5% for Fuzzy and by 10% for M-, when number of nodes is 150 the no of hops to sink decreases by 3.22% for Fuzzy and by 2.70% for M-, when number of nodes is 200 by 2.03% for Fuzzy and M-, when number of nodes is 250 by 3.44% for Fuzzy and by 8.41% for M-, and when number of nodes is 300 by 1.89% Fuzzy and by 4.81% for M-.

15 111 Table 5.5 Jitter Number of nodes Fuzzy M Fuzzy GA M Fuzzy Fuzzy GA Jitter Number of Nodes Fuzzy M Fuzzy GA M Fuzzy Fuzzy GA Figure 5.7 Jitter Experimental results conducted for Energy Management using Fuzzy Genetic Approach in WSN showed in Table 5.5 and Figure 5.7 enlighten that the jitter decreases when compared to M- and Fuzzy. When number of nodes is 50 jitter decreases by 8.47% for Fuzzy GA when compared to Fuzzy and by 19.40% for M-, when number of nodes is 100 by 7.21% for Fuzzy and by 18.18% for M-, when number of nodes is 150 by 17.39% for Fuzzy and by 27.13% for M-, when

16 112 number of nodes is 200 by 12.33% for Fuzzy and by 22.70% for M-, when number of nodes is 250 by 30.23% for Fuzzy and by 38.47% for M-, and when number of nodes is 300 by 24.47% Fuzzy and by 33.42% for M-. Table 5.6 Energy in joule per packet Number of nodes Fuzzy M Fuzzy GA M Fuzzy Fuzzy GA Energy (Joules/packet) Number of Nodes Fuzzy M Fuzzy GA M Fuzzy Fuzzy GA Figure 5.8 Energy in joule per packet Experimental results conducted for Energy Management using Fuzzy Genetic Approach in WSN showed in Table 5.6 and Figure 5.8

17 113 enlighten that the Energy in joule per packet increases for Fuzzy GA when compared to M- and Fuzzy. When number of nodes is 50 Energy in joule per packet increases by 4.50% for Fuzzy GA when compared to Fuzzy and by 1.35% for M-, when number of nodes is 100 by 4.09% for Fuzzy and by 1.22% for M-, when number of nodes is 150 by 4% for Fuzzy and by 1.20% for M-, when number of nodes is 200 by 4.42% for Fuzzy and by 1.10% for M-, when number of nodes is 250 by 3.92% for Fuzzy and by 1.07% for M-, and when number of nodes is 300 by 3.65% Fuzzy and by 0.99% for M CONCLUSIONS A fuzzy logic genetic approach is proposed for efficient energy management. Possible fuzzy rules are formed based on the number of packets to be transferred, available energy in the node and the number of hops to reach the destination. Best rule is selected by using genetic approach. Simulations were conducted using the m-leach algorithm and proposed fuzzy genetic approach. Results show that fuzzy genetic algorithm performs better than m-leach algorithm.

Genetic Algorithms. Kang Zheng Karl Schober

Genetic Algorithms. Kang Zheng Karl Schober Genetic Algorithms Kang Zheng Karl Schober Genetic algorithm What is Genetic algorithm? A genetic algorithm (or GA) is a search technique used in computing to find true or approximate solutions to optimization

More information

The k-means Algorithm and Genetic Algorithm

The k-means Algorithm and Genetic Algorithm The k-means Algorithm and Genetic Algorithm k-means algorithm Genetic algorithm Rough set approach Fuzzy set approaches Chapter 8 2 The K-Means Algorithm The K-Means algorithm is a simple yet effective

More information

Lecture 6: Genetic Algorithm. An Introduction to Meta-Heuristics, Produced by Qiangfu Zhao (Since 2012), All rights reserved

Lecture 6: Genetic Algorithm. An Introduction to Meta-Heuristics, Produced by Qiangfu Zhao (Since 2012), All rights reserved Lecture 6: Genetic Algorithm An Introduction to Meta-Heuristics, Produced by Qiangfu Zhao (Since 2012), All rights reserved Lec06/1 Search and optimization again Given a problem, the set of all possible

More information

Artificial Intelligence Application (Genetic Algorithm)

Artificial Intelligence Application (Genetic Algorithm) Babylon University College of Information Technology Software Department Artificial Intelligence Application (Genetic Algorithm) By Dr. Asaad Sabah Hadi 2014-2015 EVOLUTIONARY ALGORITHM The main idea about

More information

Mutations for Permutations

Mutations for Permutations Mutations for Permutations Insert mutation: Pick two allele values at random Move the second to follow the first, shifting the rest along to accommodate Note: this preserves most of the order and adjacency

More information

Introduction to Genetic Algorithms. Genetic Algorithms

Introduction to Genetic Algorithms. Genetic Algorithms Introduction to Genetic Algorithms Genetic Algorithms We ve covered enough material that we can write programs that use genetic algorithms! More advanced example of using arrays Could be better written

More information

Introduction to Genetic Algorithms. Based on Chapter 10 of Marsland Chapter 9 of Mitchell

Introduction to Genetic Algorithms. Based on Chapter 10 of Marsland Chapter 9 of Mitchell Introduction to Genetic Algorithms Based on Chapter 10 of Marsland Chapter 9 of Mitchell Genetic Algorithms - History Pioneered by John Holland in the 1970s Became popular in the late 1980s Based on ideas

More information

Genetic Algorithms Variations and Implementation Issues

Genetic Algorithms Variations and Implementation Issues Genetic Algorithms Variations and Implementation Issues CS 431 Advanced Topics in AI Classic Genetic Algorithms GAs as proposed by Holland had the following properties: Randomly generated population Binary

More information

A Genetic Algorithm for Graph Matching using Graph Node Characteristics 1 2

A Genetic Algorithm for Graph Matching using Graph Node Characteristics 1 2 Chapter 5 A Genetic Algorithm for Graph Matching using Graph Node Characteristics 1 2 Graph Matching has attracted the exploration of applying new computing paradigms because of the large number of applications

More information

March 19, Heuristics for Optimization. Outline. Problem formulation. Genetic algorithms

March 19, Heuristics for Optimization. Outline. Problem formulation. Genetic algorithms Olga Galinina olga.galinina@tut.fi ELT-53656 Network Analysis and Dimensioning II Department of Electronics and Communications Engineering Tampere University of Technology, Tampere, Finland March 19, 2014

More information

Evolutionary Computation. Chao Lan

Evolutionary Computation. Chao Lan Evolutionary Computation Chao Lan Outline Introduction Genetic Algorithm Evolutionary Strategy Genetic Programming Introduction Evolutionary strategy can jointly optimize multiple variables. - e.g., max

More information

Chapter 9: Genetic Algorithms

Chapter 9: Genetic Algorithms Computational Intelligence: Second Edition Contents Compact Overview First proposed by Fraser in 1957 Later by Bremermann in 1962 and Reed et al in 1967 Popularized by Holland in 1975 Genetic algorithms

More information

GENETIC ALGORITHM with Hands-On exercise

GENETIC ALGORITHM with Hands-On exercise GENETIC ALGORITHM with Hands-On exercise Adopted From Lecture by Michael Negnevitsky, Electrical Engineering & Computer Science University of Tasmania 1 Objective To understand the processes ie. GAs Basic

More information

A Genetic Algorithm-Based Approach for Energy- Efficient Clustering of Wireless Sensor Networks

A Genetic Algorithm-Based Approach for Energy- Efficient Clustering of Wireless Sensor Networks A Genetic Algorithm-Based Approach for Energy- Efficient Clustering of Wireless Sensor Networks A. Zahmatkesh and M. H. Yaghmaee Abstract In this paper, we propose a Genetic Algorithm (GA) to optimize

More information

Outline. Motivation. Introduction of GAs. Genetic Algorithm 9/7/2017. Motivation Genetic algorithms An illustrative example Hypothesis space search

Outline. Motivation. Introduction of GAs. Genetic Algorithm 9/7/2017. Motivation Genetic algorithms An illustrative example Hypothesis space search Outline Genetic Algorithm Motivation Genetic algorithms An illustrative example Hypothesis space search Motivation Evolution is known to be a successful, robust method for adaptation within biological

More information

Neural Network Weight Selection Using Genetic Algorithms

Neural Network Weight Selection Using Genetic Algorithms Neural Network Weight Selection Using Genetic Algorithms David Montana presented by: Carl Fink, Hongyi Chen, Jack Cheng, Xinglong Li, Bruce Lin, Chongjie Zhang April 12, 2005 1 Neural Networks Neural networks

More information

Research Article Path Planning Using a Hybrid Evolutionary Algorithm Based on Tree Structure Encoding

Research Article Path Planning Using a Hybrid Evolutionary Algorithm Based on Tree Structure Encoding e Scientific World Journal, Article ID 746260, 8 pages http://dx.doi.org/10.1155/2014/746260 Research Article Path Planning Using a Hybrid Evolutionary Algorithm Based on Tree Structure Encoding Ming-Yi

More information

Suppose you have a problem You don t know how to solve it What can you do? Can you use a computer to somehow find a solution for you?

Suppose you have a problem You don t know how to solve it What can you do? Can you use a computer to somehow find a solution for you? Gurjit Randhawa Suppose you have a problem You don t know how to solve it What can you do? Can you use a computer to somehow find a solution for you? This would be nice! Can it be done? A blind generate

More information

Escaping Local Optima: Genetic Algorithm

Escaping Local Optima: Genetic Algorithm Artificial Intelligence Escaping Local Optima: Genetic Algorithm Dae-Won Kim School of Computer Science & Engineering Chung-Ang University We re trying to escape local optima To achieve this, we have learned

More information

A Steady-State Genetic Algorithm for Traveling Salesman Problem with Pickup and Delivery

A Steady-State Genetic Algorithm for Traveling Salesman Problem with Pickup and Delivery A Steady-State Genetic Algorithm for Traveling Salesman Problem with Pickup and Delivery Monika Sharma 1, Deepak Sharma 2 1 Research Scholar Department of Computer Science and Engineering, NNSS SGI Samalkha,

More information

Evolutionary Algorithms. CS Evolutionary Algorithms 1

Evolutionary Algorithms. CS Evolutionary Algorithms 1 Evolutionary Algorithms CS 478 - Evolutionary Algorithms 1 Evolutionary Computation/Algorithms Genetic Algorithms l Simulate natural evolution of structures via selection and reproduction, based on performance

More information

AN EVOLUTIONARY APPROACH TO DISTANCE VECTOR ROUTING

AN EVOLUTIONARY APPROACH TO DISTANCE VECTOR ROUTING International Journal of Latest Research in Science and Technology Volume 3, Issue 3: Page No. 201-205, May-June 2014 http://www.mnkjournals.com/ijlrst.htm ISSN (Online):2278-5299 AN EVOLUTIONARY APPROACH

More information

Machine Evolution. Machine Evolution. Let s look at. Machine Evolution. Machine Evolution. Machine Evolution. Machine Evolution

Machine Evolution. Machine Evolution. Let s look at. Machine Evolution. Machine Evolution. Machine Evolution. Machine Evolution Let s look at As you will see later in this course, neural networks can learn, that is, adapt to given constraints. For example, NNs can approximate a given function. In biology, such learning corresponds

More information

Genetic Programming. Charles Chilaka. Department of Computational Science Memorial University of Newfoundland

Genetic Programming. Charles Chilaka. Department of Computational Science Memorial University of Newfoundland Genetic Programming Charles Chilaka Department of Computational Science Memorial University of Newfoundland Class Project for Bio 4241 March 27, 2014 Charles Chilaka (MUN) Genetic algorithms and programming

More information

Evolutionary Computation Algorithms for Cryptanalysis: A Study

Evolutionary Computation Algorithms for Cryptanalysis: A Study Evolutionary Computation Algorithms for Cryptanalysis: A Study Poonam Garg Information Technology and Management Dept. Institute of Management Technology Ghaziabad, India pgarg@imt.edu Abstract The cryptanalysis

More information

Clustering Analysis of Simple K Means Algorithm for Various Data Sets in Function Optimization Problem (Fop) of Evolutionary Programming

Clustering Analysis of Simple K Means Algorithm for Various Data Sets in Function Optimization Problem (Fop) of Evolutionary Programming Clustering Analysis of Simple K Means Algorithm for Various Data Sets in Function Optimization Problem (Fop) of Evolutionary Programming R. Karthick 1, Dr. Malathi.A 2 Research Scholar, Department of Computer

More information

An Improved Genetic Algorithm based Fault tolerance Method for distributed wireless sensor networks.

An Improved Genetic Algorithm based Fault tolerance Method for distributed wireless sensor networks. An Improved Genetic Algorithm based Fault tolerance Method for distributed wireless sensor networks. Anagha Nanoti, Prof. R. K. Krishna M.Tech student in Department of Computer Science 1, Department of

More information

The Genetic Algorithm for finding the maxima of single-variable functions

The Genetic Algorithm for finding the maxima of single-variable functions Research Inventy: International Journal Of Engineering And Science Vol.4, Issue 3(March 2014), PP 46-54 Issn (e): 2278-4721, Issn (p):2319-6483, www.researchinventy.com The Genetic Algorithm for finding

More information

Hybridization EVOLUTIONARY COMPUTING. Reasons for Hybridization - 1. Naming. Reasons for Hybridization - 3. Reasons for Hybridization - 2

Hybridization EVOLUTIONARY COMPUTING. Reasons for Hybridization - 1. Naming. Reasons for Hybridization - 3. Reasons for Hybridization - 2 Hybridization EVOLUTIONARY COMPUTING Hybrid Evolutionary Algorithms hybridization of an EA with local search techniques (commonly called memetic algorithms) EA+LS=MA constructive heuristics exact methods

More information

Genetic Algorithms. Genetic Algorithms

Genetic Algorithms. Genetic Algorithms A biological analogy for optimization problems Bit encoding, models as strings Reproduction and mutation -> natural selection Pseudo-code for a simple genetic algorithm The goal of genetic algorithms (GA):

More information

CHAPTER 5 ANT-FUZZY META HEURISTIC GENETIC SENSOR NETWORK SYSTEM FOR MULTI - SINK AGGREGATED DATA TRANSMISSION

CHAPTER 5 ANT-FUZZY META HEURISTIC GENETIC SENSOR NETWORK SYSTEM FOR MULTI - SINK AGGREGATED DATA TRANSMISSION CHAPTER 5 ANT-FUZZY META HEURISTIC GENETIC SENSOR NETWORK SYSTEM FOR MULTI - SINK AGGREGATED DATA TRANSMISSION 5.1 INTRODUCTION Generally, deployment of Wireless Sensor Network (WSN) is based on a many

More information

Role of Genetic Algorithm in Routing for Large Network

Role of Genetic Algorithm in Routing for Large Network Role of Genetic Algorithm in Routing for Large Network *Mr. Kuldeep Kumar, Computer Programmer, Krishi Vigyan Kendra, CCS Haryana Agriculture University, Hisar. Haryana, India verma1.kuldeep@gmail.com

More information

1. Introduction. 2. Motivation and Problem Definition. Volume 8 Issue 2, February Susmita Mohapatra

1. Introduction. 2. Motivation and Problem Definition. Volume 8 Issue 2, February Susmita Mohapatra Pattern Recall Analysis of the Hopfield Neural Network with a Genetic Algorithm Susmita Mohapatra Department of Computer Science, Utkal University, India Abstract: This paper is focused on the implementation

More information

Introduction to Evolutionary Computation

Introduction to Evolutionary Computation Introduction to Evolutionary Computation The Brought to you by (insert your name) The EvoNet Training Committee Some of the Slides for this lecture were taken from the Found at: www.cs.uh.edu/~ceick/ai/ec.ppt

More information

Genetic Algorithms for Vision and Pattern Recognition

Genetic Algorithms for Vision and Pattern Recognition Genetic Algorithms for Vision and Pattern Recognition Faiz Ul Wahab 11/8/2014 1 Objective To solve for optimization of computer vision problems using genetic algorithms 11/8/2014 2 Timeline Problem: Computer

More information

Grid Scheduling Strategy using GA (GSSGA)

Grid Scheduling Strategy using GA (GSSGA) F Kurus Malai Selvi et al,int.j.computer Technology & Applications,Vol 3 (5), 8-86 ISSN:2229-693 Grid Scheduling Strategy using GA () Dr.D.I.George Amalarethinam Director-MCA & Associate Professor of Computer

More information

An Energy Efficient Multicast Routing Based On Genetic Algorithm for MANET

An Energy Efficient Multicast Routing Based On Genetic Algorithm for MANET An Energy Efficient Multicast Routing Based On Genetic Algorithm for MANET P.Prasanna 1, D.Saravanan 2, RM.Chandrasekaran 3 PG scholar, Pavendar Bharathidasan College of Engg and Tech, Tiruchirappalli,

More information

Adaptive Crossover in Genetic Algorithms Using Statistics Mechanism

Adaptive Crossover in Genetic Algorithms Using Statistics Mechanism in Artificial Life VIII, Standish, Abbass, Bedau (eds)(mit Press) 2002. pp 182 185 1 Adaptive Crossover in Genetic Algorithms Using Statistics Mechanism Shengxiang Yang Department of Mathematics and Computer

More information

Network Routing Protocol using Genetic Algorithms

Network Routing Protocol using Genetic Algorithms International Journal of Electrical & Computer Sciences IJECS-IJENS Vol:0 No:02 40 Network Routing Protocol using Genetic Algorithms Gihan Nagib and Wahied G. Ali Abstract This paper aims to develop a

More information

International Journal of Digital Application & Contemporary research Website: (Volume 1, Issue 7, February 2013)

International Journal of Digital Application & Contemporary research Website:   (Volume 1, Issue 7, February 2013) Performance Analysis of GA and PSO over Economic Load Dispatch Problem Sakshi Rajpoot sakshirajpoot1988@gmail.com Dr. Sandeep Bhongade sandeepbhongade@rediffmail.com Abstract Economic Load dispatch problem

More information

Introduction to Design Optimization: Search Methods

Introduction to Design Optimization: Search Methods Introduction to Design Optimization: Search Methods 1-D Optimization The Search We don t know the curve. Given α, we can calculate f(α). By inspecting some points, we try to find the approximated shape

More information

ARTIFICIAL INTELLIGENCE (CSCU9YE ) LECTURE 5: EVOLUTIONARY ALGORITHMS

ARTIFICIAL INTELLIGENCE (CSCU9YE ) LECTURE 5: EVOLUTIONARY ALGORITHMS ARTIFICIAL INTELLIGENCE (CSCU9YE ) LECTURE 5: EVOLUTIONARY ALGORITHMS Gabriela Ochoa http://www.cs.stir.ac.uk/~goc/ OUTLINE Optimisation problems Optimisation & search Two Examples The knapsack problem

More information

A New Selection Operator - CSM in Genetic Algorithms for Solving the TSP

A New Selection Operator - CSM in Genetic Algorithms for Solving the TSP A New Selection Operator - CSM in Genetic Algorithms for Solving the TSP Wael Raef Alkhayri Fahed Al duwairi High School Aljabereyah, Kuwait Suhail Sami Owais Applied Science Private University Amman,

More information

Introduction to Genetic Algorithms

Introduction to Genetic Algorithms Advanced Topics in Image Analysis and Machine Learning Introduction to Genetic Algorithms Week 3 Faculty of Information Science and Engineering Ritsumeikan University Today s class outline Genetic Algorithms

More information

Information Fusion Dr. B. K. Panigrahi

Information Fusion Dr. B. K. Panigrahi Information Fusion By Dr. B. K. Panigrahi Asst. Professor Department of Electrical Engineering IIT Delhi, New Delhi-110016 01/12/2007 1 Introduction Classification OUTLINE K-fold cross Validation Feature

More information

Monika Maharishi Dayanand University Rohtak

Monika Maharishi Dayanand University Rohtak Performance enhancement for Text Data Mining using k means clustering based genetic optimization (KMGO) Monika Maharishi Dayanand University Rohtak ABSTRACT For discovering hidden patterns and structures

More information

Genetic Tuning for Improving Wang and Mendel s Fuzzy Database

Genetic Tuning for Improving Wang and Mendel s Fuzzy Database Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics San Antonio, TX, USA - October 2009 Genetic Tuning for Improving Wang and Mendel s Fuzzy Database E. R. R. Kato, O.

More information

Genetic Algorithm for Dynamic Capacitated Minimum Spanning Tree

Genetic Algorithm for Dynamic Capacitated Minimum Spanning Tree 28 Genetic Algorithm for Dynamic Capacitated Minimum Spanning Tree 1 Tanu Gupta, 2 Anil Kumar 1 Research Scholar, IFTM, University, Moradabad, India. 2 Sr. Lecturer, KIMT, Moradabad, India. Abstract Many

More information

Genetic-PSO Fuzzy Data Mining With Divide and Conquer Strategy

Genetic-PSO Fuzzy Data Mining With Divide and Conquer Strategy Genetic-PSO Fuzzy Data Mining With Divide and Conquer Strategy Amin Jourabloo Department of Computer Engineering, Sharif University of Technology, Tehran, Iran E-mail: jourabloo@ce.sharif.edu Abstract

More information

GENETIC ALGORITHM VERSUS PARTICLE SWARM OPTIMIZATION IN N-QUEEN PROBLEM

GENETIC ALGORITHM VERSUS PARTICLE SWARM OPTIMIZATION IN N-QUEEN PROBLEM Journal of Al-Nahrain University Vol.10(2), December, 2007, pp.172-177 Science GENETIC ALGORITHM VERSUS PARTICLE SWARM OPTIMIZATION IN N-QUEEN PROBLEM * Azhar W. Hammad, ** Dr. Ban N. Thannoon Al-Nahrain

More information

Using Simple Ancestry to Deter Inbreeding for Persistent Genetic Algorithm Search

Using Simple Ancestry to Deter Inbreeding for Persistent Genetic Algorithm Search Using Simple Ancestry to Deter Inbreeding for Persistent Genetic Algorithm Search Aditya Wibowo and Peter Jamieson Dept. of Electrical and Computer Engineering Miami University Abstract In this work, we

More information

A GENETIC ALGORITHM FOR CLUSTERING ON VERY LARGE DATA SETS

A GENETIC ALGORITHM FOR CLUSTERING ON VERY LARGE DATA SETS A GENETIC ALGORITHM FOR CLUSTERING ON VERY LARGE DATA SETS Jim Gasvoda and Qin Ding Department of Computer Science, Pennsylvania State University at Harrisburg, Middletown, PA 17057, USA {jmg289, qding}@psu.edu

More information

CHAPTER 2 CONVENTIONAL AND NON-CONVENTIONAL TECHNIQUES TO SOLVE ORPD PROBLEM

CHAPTER 2 CONVENTIONAL AND NON-CONVENTIONAL TECHNIQUES TO SOLVE ORPD PROBLEM 20 CHAPTER 2 CONVENTIONAL AND NON-CONVENTIONAL TECHNIQUES TO SOLVE ORPD PROBLEM 2.1 CLASSIFICATION OF CONVENTIONAL TECHNIQUES Classical optimization methods can be classified into two distinct groups:

More information

Heuristic Optimisation

Heuristic Optimisation Heuristic Optimisation Part 10: Genetic Algorithm Basics Sándor Zoltán Németh http://web.mat.bham.ac.uk/s.z.nemeth s.nemeth@bham.ac.uk University of Birmingham S Z Németh (s.nemeth@bham.ac.uk) Heuristic

More information

Meta- Heuristic based Optimization Algorithms: A Comparative Study of Genetic Algorithm and Particle Swarm Optimization

Meta- Heuristic based Optimization Algorithms: A Comparative Study of Genetic Algorithm and Particle Swarm Optimization 2017 2 nd International Electrical Engineering Conference (IEEC 2017) May. 19 th -20 th, 2017 at IEP Centre, Karachi, Pakistan Meta- Heuristic based Optimization Algorithms: A Comparative Study of Genetic

More information

The Parallel Software Design Process. Parallel Software Design

The Parallel Software Design Process. Parallel Software Design Parallel Software Design The Parallel Software Design Process Deborah Stacey, Chair Dept. of Comp. & Info Sci., University of Guelph dastacey@uoguelph.ca Why Parallel? Why NOT Parallel? Why Talk about

More information

METAHEURISTICS Genetic Algorithm

METAHEURISTICS Genetic Algorithm METAHEURISTICS Genetic Algorithm Jacques A. Ferland Department of Informatique and Recherche Opérationnelle Université de Montréal ferland@iro.umontreal.ca Genetic Algorithm (GA) Population based algorithm

More information

4/22/2014. Genetic Algorithms. Diwakar Yagyasen Department of Computer Science BBDNITM. Introduction

4/22/2014. Genetic Algorithms. Diwakar Yagyasen Department of Computer Science BBDNITM. Introduction 4/22/24 s Diwakar Yagyasen Department of Computer Science BBDNITM Visit dylycknow.weebly.com for detail 2 The basic purpose of a genetic algorithm () is to mimic Nature s evolutionary approach The algorithm

More information

Planning and Search. Genetic algorithms. Genetic algorithms 1

Planning and Search. Genetic algorithms. Genetic algorithms 1 Planning and Search Genetic algorithms Genetic algorithms 1 Outline Genetic algorithms Representing states (individuals, or chromosomes) Genetic operations (mutation, crossover) Example Genetic algorithms

More information

Evolutionary Computation Part 2

Evolutionary Computation Part 2 Evolutionary Computation Part 2 CS454, Autumn 2017 Shin Yoo (with some slides borrowed from Seongmin Lee @ COINSE) Crossover Operators Offsprings inherit genes from their parents, but not in identical

More information

Comparative Analysis of Genetic Algorithm Implementations

Comparative Analysis of Genetic Algorithm Implementations Comparative Analysis of Genetic Algorithm Implementations Robert Soricone Dr. Melvin Neville Department of Computer Science Northern Arizona University Flagstaff, Arizona SIGAda 24 Outline Introduction

More information

Introduction to Design Optimization: Search Methods

Introduction to Design Optimization: Search Methods Introduction to Design Optimization: Search Methods 1-D Optimization The Search We don t know the curve. Given α, we can calculate f(α). By inspecting some points, we try to find the approximated shape

More information

Preprocessing of Stream Data using Attribute Selection based on Survival of the Fittest

Preprocessing of Stream Data using Attribute Selection based on Survival of the Fittest Preprocessing of Stream Data using Attribute Selection based on Survival of the Fittest Bhakti V. Gavali 1, Prof. Vivekanand Reddy 2 1 Department of Computer Science and Engineering, Visvesvaraya Technological

More information

Adaptive design optimization of wireless sensor networks using genetic algorithms q

Adaptive design optimization of wireless sensor networks using genetic algorithms q Computer Networks 51 (2007) 1031 1051 www.elsevier.com/locate/comnet Adaptive design optimization of wireless sensor networks using genetic algorithms q Konstantinos P. Ferentinos *, Theodore A. Tsiligiridis

More information

Intra and Inter Cluster Synchronization Scheme for Cluster Based Sensor Network

Intra and Inter Cluster Synchronization Scheme for Cluster Based Sensor Network Intra and Inter Cluster Synchronization Scheme for Cluster Based Sensor Network V. Shunmuga Sundari 1, N. Mymoon Zuviria 2 1 Student, 2 Asisstant Professor, Computer Science and Engineering, National College

More information

CS5401 FS2015 Exam 1 Key

CS5401 FS2015 Exam 1 Key CS5401 FS2015 Exam 1 Key This is a closed-book, closed-notes exam. The only items you are allowed to use are writing implements. Mark each sheet of paper you use with your name and the string cs5401fs2015

More information

Genetic Algorithm for Finding Shortest Path in a Network

Genetic Algorithm for Finding Shortest Path in a Network Intern. J. Fuzzy Mathematical Archive Vol. 2, 2013, 43-48 ISSN: 2320 3242 (P), 2320 3250 (online) Published on 26 August 2013 www.researchmathsci.org International Journal of Genetic Algorithm for Finding

More information

Using Genetic Algorithms in Integer Programming for Decision Support

Using Genetic Algorithms in Integer Programming for Decision Support Doi:10.5901/ajis.2014.v3n6p11 Abstract Using Genetic Algorithms in Integer Programming for Decision Support Dr. Youcef Souar Omar Mouffok Taher Moulay University Saida, Algeria Email:Syoucef12@yahoo.fr

More information

Khushboo Arora, Samiksha Agarwal, Rohit Tanwar

Khushboo Arora, Samiksha Agarwal, Rohit Tanwar International Journal of Scientific & Engineering Research, Volume 7, Issue 1, January-2016 1014 Solving TSP using Genetic Algorithm and Nearest Neighbour Algorithm and their Comparison Khushboo Arora,

More information

CONCEPT FORMATION AND DECISION TREE INDUCTION USING THE GENETIC PROGRAMMING PARADIGM

CONCEPT FORMATION AND DECISION TREE INDUCTION USING THE GENETIC PROGRAMMING PARADIGM 1 CONCEPT FORMATION AND DECISION TREE INDUCTION USING THE GENETIC PROGRAMMING PARADIGM John R. Koza Computer Science Department Stanford University Stanford, California 94305 USA E-MAIL: Koza@Sunburn.Stanford.Edu

More information

Regression Test Case Prioritization using Genetic Algorithm

Regression Test Case Prioritization using Genetic Algorithm 9International Journal of Current Trends in Engineering & Research (IJCTER) e-issn 2455 1392 Volume 2 Issue 8, August 2016 pp. 9 16 Scientific Journal Impact Factor : 3.468 http://www.ijcter.com Regression

More information

MINIMAL EDGE-ORDERED SPANNING TREES USING A SELF-ADAPTING GENETIC ALGORITHM WITH MULTIPLE GENOMIC REPRESENTATIONS

MINIMAL EDGE-ORDERED SPANNING TREES USING A SELF-ADAPTING GENETIC ALGORITHM WITH MULTIPLE GENOMIC REPRESENTATIONS Proceedings of Student/Faculty Research Day, CSIS, Pace University, May 5 th, 2006 MINIMAL EDGE-ORDERED SPANNING TREES USING A SELF-ADAPTING GENETIC ALGORITHM WITH MULTIPLE GENOMIC REPRESENTATIONS Richard

More information

Genetic-Algorithm-Based Construction of Load-Balanced CDSs in Wireless Sensor Networks

Genetic-Algorithm-Based Construction of Load-Balanced CDSs in Wireless Sensor Networks Genetic-Algorithm-Based Construction of Load-Balanced CDSs in Wireless Sensor Networks Jing He, Shouling Ji, Mingyuan Yan, Yi Pan, and Yingshu Li Department of Computer Science Georgia State University,

More information

RESOLVING AMBIGUITIES IN PREPOSITION PHRASE USING GENETIC ALGORITHM

RESOLVING AMBIGUITIES IN PREPOSITION PHRASE USING GENETIC ALGORITHM International Journal of Computer Engineering and Applications, Volume VIII, Issue III, December 14 RESOLVING AMBIGUITIES IN PREPOSITION PHRASE USING GENETIC ALGORITHM Department of Computer Engineering,

More information

Introduction to Optimization

Introduction to Optimization Introduction to Optimization Approximation Algorithms and Heuristics November 21, 2016 École Centrale Paris, Châtenay-Malabry, France Dimo Brockhoff Inria Saclay Ile-de-France 2 Exercise: The Knapsack

More information

Distributed Optimization of Feature Mining Using Evolutionary Techniques

Distributed Optimization of Feature Mining Using Evolutionary Techniques Distributed Optimization of Feature Mining Using Evolutionary Techniques Karthik Ganesan Pillai University of Dayton Computer Science 300 College Park Dayton, OH 45469-2160 Dale Emery Courte University

More information

AN IMPROVED ITERATIVE METHOD FOR SOLVING GENERAL SYSTEM OF EQUATIONS VIA GENETIC ALGORITHMS

AN IMPROVED ITERATIVE METHOD FOR SOLVING GENERAL SYSTEM OF EQUATIONS VIA GENETIC ALGORITHMS AN IMPROVED ITERATIVE METHOD FOR SOLVING GENERAL SYSTEM OF EQUATIONS VIA GENETIC ALGORITHMS Seyed Abolfazl Shahzadehfazeli 1, Zainab Haji Abootorabi,3 1 Parallel Processing Laboratory, Yazd University,

More information

An Evolutionary Algorithm for the Multi-objective Shortest Path Problem

An Evolutionary Algorithm for the Multi-objective Shortest Path Problem An Evolutionary Algorithm for the Multi-objective Shortest Path Problem Fangguo He Huan Qi Qiong Fan Institute of Systems Engineering, Huazhong University of Science & Technology, Wuhan 430074, P. R. China

More information

Comparative Study on VQ with Simple GA and Ordain GA

Comparative Study on VQ with Simple GA and Ordain GA Proceedings of the 9th WSEAS International Conference on Automatic Control, Modeling & Simulation, Istanbul, Turkey, May 27-29, 2007 204 Comparative Study on VQ with Simple GA and Ordain GA SADAF SAJJAD

More information

Topological Machining Fixture Layout Synthesis Using Genetic Algorithms

Topological Machining Fixture Layout Synthesis Using Genetic Algorithms Topological Machining Fixture Layout Synthesis Using Genetic Algorithms Necmettin Kaya Uludag University, Mechanical Eng. Department, Bursa, Turkey Ferruh Öztürk Uludag University, Mechanical Eng. Department,

More information

Automatic Selection of GCC Optimization Options Using A Gene Weighted Genetic Algorithm

Automatic Selection of GCC Optimization Options Using A Gene Weighted Genetic Algorithm Automatic Selection of GCC Optimization Options Using A Gene Weighted Genetic Algorithm San-Chih Lin, Chi-Kuang Chang, Nai-Wei Lin National Chung Cheng University Chiayi, Taiwan 621, R.O.C. {lsch94,changck,naiwei}@cs.ccu.edu.tw

More information

Genetic Algorithms. Chapter 3

Genetic Algorithms. Chapter 3 Chapter 3 1 Contents of this Chapter 2 Introductory example. Representation of individuals: Binary, integer, real-valued, and permutation. Mutation operator. Mutation for binary, integer, real-valued,

More information

System of Systems Architecture Generation and Evaluation using Evolutionary Algorithms

System of Systems Architecture Generation and Evaluation using Evolutionary Algorithms SysCon 2008 IEEE International Systems Conference Montreal, Canada, April 7 10, 2008 System of Systems Architecture Generation and Evaluation using Evolutionary Algorithms Joseph J. Simpson 1, Dr. Cihan

More information

Basic Data Mining Technique

Basic Data Mining Technique Basic Data Mining Technique What is classification? What is prediction? Supervised and Unsupervised Learning Decision trees Association rule K-nearest neighbor classifier Case-based reasoning Genetic algorithm

More information

Resolving the Conflict Between Competitive and Cooperative Behavior in Michigan-Type Fuzzy Classifier Systems

Resolving the Conflict Between Competitive and Cooperative Behavior in Michigan-Type Fuzzy Classifier Systems Resolving the Conflict Between Competitive and Cooperative Behavior in Michigan-Type Fuzzy Classifier Systems Peter Haslinger and Ulrich Bodenhofer Software Competence Center Hagenberg A-4232 Hagenberg,

More information

CHAPTER 6 HYBRID AI BASED IMAGE CLASSIFICATION TECHNIQUES

CHAPTER 6 HYBRID AI BASED IMAGE CLASSIFICATION TECHNIQUES CHAPTER 6 HYBRID AI BASED IMAGE CLASSIFICATION TECHNIQUES 6.1 INTRODUCTION The exploration of applications of ANN for image classification has yielded satisfactory results. But, the scope for improving

More information

A Web-Based Evolutionary Algorithm Demonstration using the Traveling Salesman Problem

A Web-Based Evolutionary Algorithm Demonstration using the Traveling Salesman Problem A Web-Based Evolutionary Algorithm Demonstration using the Traveling Salesman Problem Richard E. Mowe Department of Statistics St. Cloud State University mowe@stcloudstate.edu Bryant A. Julstrom Department

More information

Time Complexity Analysis of the Genetic Algorithm Clustering Method

Time Complexity Analysis of the Genetic Algorithm Clustering Method Time Complexity Analysis of the Genetic Algorithm Clustering Method Z. M. NOPIAH, M. I. KHAIRIR, S. ABDULLAH, M. N. BAHARIN, and A. ARIFIN Department of Mechanical and Materials Engineering Universiti

More information

Evolutionary design for the behaviour of cellular automaton-based complex systems

Evolutionary design for the behaviour of cellular automaton-based complex systems Evolutionary design for the behaviour of cellular automaton-based complex systems School of Computer Science & IT University of Nottingham Adaptive Computing in Design and Manufacture Bristol Motivation

More information

Improving interpretability in approximative fuzzy models via multi-objective evolutionary algorithms.

Improving interpretability in approximative fuzzy models via multi-objective evolutionary algorithms. Improving interpretability in approximative fuzzy models via multi-objective evolutionary algorithms. Gómez-Skarmeta, A.F. University of Murcia skarmeta@dif.um.es Jiménez, F. University of Murcia fernan@dif.um.es

More information

CHAPTER 4 FEATURE SELECTION USING GENETIC ALGORITHM

CHAPTER 4 FEATURE SELECTION USING GENETIC ALGORITHM CHAPTER 4 FEATURE SELECTION USING GENETIC ALGORITHM In this research work, Genetic Algorithm method is used for feature selection. The following section explains how Genetic Algorithm is used for feature

More information

MDL-based Genetic Programming for Object Detection

MDL-based Genetic Programming for Object Detection MDL-based Genetic Programming for Object Detection Yingqiang Lin and Bir Bhanu Center for Research in Intelligent Systems University of California, Riverside, CA, 92521, USA Email: {yqlin, bhanu}@vislab.ucr.edu

More information

Similarity Templates or Schemata. CS 571 Evolutionary Computation

Similarity Templates or Schemata. CS 571 Evolutionary Computation Similarity Templates or Schemata CS 571 Evolutionary Computation Similarities among Strings in a Population A GA has a population of strings (solutions) that change from generation to generation. What

More information

A Parallel Architecture for the Generalized Traveling Salesman Problem

A Parallel Architecture for the Generalized Traveling Salesman Problem A Parallel Architecture for the Generalized Traveling Salesman Problem Max Scharrenbroich AMSC 663 Project Proposal Advisor: Dr. Bruce L. Golden R. H. Smith School of Business 1 Background and Introduction

More information

Advanced Search Genetic algorithm

Advanced Search Genetic algorithm Advanced Search Genetic algorithm Yingyu Liang yliang@cs.wisc.edu Computer Sciences Department University of Wisconsin, Madison [Based on slides from Jerry Zhu, Andrew Moore http://www.cs.cmu.edu/~awm/tutorials

More information

Introduction to Optimization

Introduction to Optimization Introduction to Optimization Approximation Algorithms and Heuristics November 6, 2015 École Centrale Paris, Châtenay-Malabry, France Dimo Brockhoff INRIA Lille Nord Europe 2 Exercise: The Knapsack Problem

More information

An Energy Efficient WSN Using Genetic Algorithm

An Energy Efficient WSN Using Genetic Algorithm An Energy Efficient WSN Using Genetic Algorithm Neema Subash Teena Abraham Dillmol Thankachan PG Student, Dept ECE Asst. Professor, Dept ECE PG Student, Dept ECE MBITS, Nellimattom MBITS, Nellimattom MBITS,

More information

Preliminary Background Tabu Search Genetic Algorithm

Preliminary Background Tabu Search Genetic Algorithm Preliminary Background Tabu Search Genetic Algorithm Faculty of Information Technology University of Science Vietnam National University of Ho Chi Minh City March 2010 Problem used to illustrate General

More information

MultiHop Routing for Delay Minimization in WSN

MultiHop Routing for Delay Minimization in WSN MultiHop Routing for Delay Minimization in WSN Sandeep Chaurasia, Saima Khan, Sudesh Gupta Abstract Wireless sensor network, consists of sensor nodes in capacity of hundred or thousand, which deployed

More information

A NOVEL APPROACH FOR PRIORTIZATION OF OPTIMIZED TEST CASES

A NOVEL APPROACH FOR PRIORTIZATION OF OPTIMIZED TEST CASES A NOVEL APPROACH FOR PRIORTIZATION OF OPTIMIZED TEST CASES Abhishek Singhal Amity School of Engineering and Technology Amity University Noida, India asinghal1@amity.edu Swati Chandna Amity School of Engineering

More information